Analytic Culture in the U.S. Intelligence Community (PDF) - CIA

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ISBN: 1-929667-13-2

ANALYTIC CULTURE IN THE US 

INTELLIGENCE COMMUNITY

The Center for the Study of Intelligence 


Washington, DC 20505 

Johnston, Rob 

Library of Congress Cataloguing-in-Publication data 

Analytic Culture in the US Intelligence Community: An Ethnographic Study/ 

Dr. Rob Johnston 

Includes bibliographic references. 

ISBN 1-929667-13-2 (pbk.:alk paper) 

1. Intelligence—United States. 2. Intelligence analysis. 

3. Intelligence policy. 4. Intelligence training. 

Typeset in Times and Ariel. 

Printed by Imaging and Publication Support, CIA. 

Cover design: Imaging and Publication Support, CIA. 

The pensive subject of the statue is Karl Ernst von Baer (1792–1876), the 

Prussian-Estonian pioneer of embryology, geography, ethnology, and physical 

anthropology (Jane M. Oppenheimer, Encyclopedia Brittanica).

ANALYTIC CULTURE IN THE 

US INTELLIGENCE COMMUNITY 

AN ETHNOGRAPHIC STUDY 

DR. ROB JOHNSTON 

Center for the Study of Intelligence 


Washington, DC 

2005

ACKNOWLEDGEMENTS 

There are literally slightly more than 1,000 people to thank for their help in 

developing this work. Most of them I cannot name, for one reason or another, 

but my thanks go out to all of those who took the time to participate in this 

research project. Thank you for your trust. Particular thanks are due the other 

researchers who coauthored chapters: Judith Meister Johnston, J. Dexter 

Fletcher, and Stephen Konya. 

There is a long list of individuals and institutions deserving of my gratitude, 

but, at the outset, for making this study possible, I would like to express my 

appreciation to Paul Johnson and Woody Kuhns, the chief and deputy chief of 

the Central Intelligence Agency’s Center for the Study of Intelligence, and 

their staff for their support and to John Phillips and Tom Kennedy of the Intelligence 

Technology Innovation Center who, along with their staff, administer 

the Director of Central Intelligence Postdoctoral Fellowship Program. 

I would like to thank Greg Treverton and Joe Hayes for their help throughout 

this project and for their willingness to give of their time. Dr. Forrest 

Frank, Charles Perrow, and Matthew Johnson deserve recognition for the 

material they contributed. Although it was not possible to cite them as references 

for those contributions, these are indicated in the footnotes, and I give 

them full credit for their work and efforts. 

I would also like to thank Bruce Berkowitz, Mike Warner, Fritz Ermarth, 

Gordon Oehler, Jeffrey Cooper, Dave Kaplan, John Morrison, James Wirtz, 

Robyn Dawes, Chris Johnson, Marilyn Peterson, Drew Cukor, Dennis 

McBride, Paul Chatelier, Stephen Marrin, Randy Good, Brian Hearing, Phil 

Williams, Jonathan Clemente, Jim Wilson, Dennis, Kowal, Randy Murch, 

Gordie Boezer, Steve Holder, Joel Resnick, Mark Stout, Mike Vlahos, Mike 

Rigdon, Jim Silk, Karl Lowe, Kevin O’Connell, Dennis Gormley, Randy 

Pherson, Chris Andrew, Daniel Serfaty, Tom Armour, Gary Klein, Brian 

Moon, Richard Hackman, Charlie Kisner, Matt McKnight, Joe Rosen, Mike 

Yared, Jen Lucas, Dick Heuer, Robert Jervis, Pam Harbourne, Katie Dorr, 

v

Dori Akerman, and, most particularly, my editors: Mike Schneider, Andy 

Vaart, and Barbara Pace. Special thanks are also due Adm. Dennis Blair, USN 

(Ret.), and Gen. Larry Welch, USAF (Ret.), and their staff. 

There were 50 peer reviewers who made sure I did not go too far afield in 

my research and analysis. Again, there are mitigating reasons why I cannot 

thank them by name. Suffice it to say, their work and their time were invaluable, 

and I appreciate their efforts. 

Because I cannot name specific individuals, I would like to thank the organizations 

of the Intelligence Community that gave me access to perform this research 

and made available research participants: Air Force Intelligence; Army Intelligence; 

Central Intelligence Agency; Defense Intelligence Agency; Department of 

Energy; Department of Homeland Security; Bureau of Intelligence and Research, 

Department of State; Department of the Treasury; Federal Bureau of Investigation; 

Marine Corps Intelligence; National Geospatial Intelligence Agency; 

National Reconnaissance Office; National Security Agency; Navy Intelligence. 

Thanks are also due the following: Institute for Defense Analyses; the Sherman 

Kent Center and the Global Futures Partnership at the CIA University; the 

CIA’s Publications Review Board; Office of Public Affairs, National Archives; 

Joint Military Intelligence College; Advanced Research and Development Activity, 

Defense Advanced Research Projects Agency (DARPA); International Association 

of Law Enforcement Intelligence Analysts; Drug Enforcement 

Administration and the DEA Academy; FBI Academy; National Military Intelligence 

Association; Association of Former Intelligence Officers; MITRE; RAND; 

Analytic Services, Inc. (ANSER); Potomac Institute; Center for Strategic and 

International Studies; Woodrow Wilson International Center; Booz Allen Hamilton; 

Naval Postgraduate School; Columbia University; Dartmouth College; University 

of Pittsburgh; Georgetown University; Carnegie Mellon University; 

Cambridge University; Johns Hopkins University and the Advanced Physics 

Laboratory; George Mason University; Harvard University; Yale University; 

American Anthropological Association; Society for the Anthropology of Work; 

Society for Applied Anthropology; National Association for the Practice of 

Anthropology; Inter-University Seminar on Armed Forces and Society; Royal 

Anthropological Institute, and the national laboratories. 

I express my sincere apologies if I have failed to include any individuals or 

organizations to which thanks are due. Moreover, any errors of commission or 

omission are my own. God knows, with this much help, there is no one to 

blame but myself. Mostly, though, I would like to thank my long-suffering 

wife, to whom this book is dedicated. Thanks, Jude. 

vi

CONTENTS 

Foreword by Gregory F. Treverton ........................................................... xi 

Introduction ............................................................................................. xiii 

Background ...................................................................................... xiv 

Scope ............................................................................................... xvii 

A Work in Progress .......................................................................... xix 

Part I: Research Findings 

Chapter One: Definitions ........................................................................... 3 

Chapter Two: Findings................................................................................ 9 

The Problem of Bias ........................................................................ 10 

Finding: Secrecy Versus Efficacy ..................................................... 11 

Finding: Time Constraints ................................................................ 13 

Finding: Focus on Current Production .............................................. 15 

Finding: Rewards and Incentives ...................................................... 16 

Finding: “Tradecraft” Versus Scientific Methodology ..................... 17 

Finding: Confirmation Bias, Norms, and Taboos ............................ 21 

Finding: Analytic Identity ................................................................ 25 

Finding: Analytic Training ............................................................... 28 

Part II: Ethnography of Analysis 

Chapter Three: A Taxonomy of Intelligence Variables ........................... 33 

Intelligence Analysis ......................................................................... 34 

Developing the Taxonomy ................................................................ 37 

Systemic Variables ........................................................................... 39 

Systematic Variables ......................................................................... 40 

Idiosyncratic Variables ..................................................................... 41 

Communicative Variables ................................................................. 42 

Conclusion ........................................................................................ 43 

Chapter Four: Testing the Intelligence Cycle 

Through Systems Modeling and Simulation ......................................... 45 

The Traditional Intelligence Cycle ................................................... 45 

Systematic Analysis .......................................................................... 47 

Findings Based on Systematic Analysis ........................................... 47 

Systemic Analysis ............................................................................. 50 

vii

Findings Based on Systems Analysis ................................................54 

Recommendations ............................................................................55 

Part III: Potential Areas for Improvement 

Chapter Five: Integrating Methodologists into Teams of Experts ............61 

Becoming an Expert .........................................................................61 

The Power of Expertise .....................................................................63 

The Paradox of Expertise ..................................................................64 

The Burden on Intelligence Analysts ................................................66 

The Pros and Cons of Teams .............................................................68 

Can Technology Help? ......................................................................71 

Analytic Methodologists ...................................................................72 

Conclusion ........................................................................................72 

Chapter Six: The Question of Foreign Cultures: 

Combating Ethnocentrism in Intelligence Analysis ...............................75 

Case Study One: Tiananmen Square .................................................76 

Case Study Two: The Red Team ......................................................81 

Conclusion and Recommendations ...................................................84 

Chapter Seven: Instructional Technology: 

Effectiveness and Implications for the Intelligence Community ...........87 

Background ........................................................................................88 

Meta-analysis Demonstrates the Effectiveness of 

Instructional Technology ............................................................90 

Current Research on Higher Cognitive Abilities ...............................92 

Discussion ..........................................................................................94 

Conclusion .........................................................................................96 

Chapter Eight: Organizational Culture: 

Anticipatory Socialization and Intelligence Analysts ............................97 

Organizational Socialization .............................................................98 

Anticipatory Socialization ................................................................99 

Consequences of Culture Mismatch ................................................101 

Anticipatory Socialization in the Intelligence Community .............102 

Conclusion and Recommendations .................................................104 

Chapter Nine: Recommendations ...........................................................107 

The First Step: Recognizing A Fundamental Problem ....................107 

Performance Improvement Infrastructure .......................................108 

Infrastructure Requirements ............................................................108 

Research Programs ..........................................................................111 

The Importance of Access ...............................................................115 

viii

Part IV: Notes on Methodology 

Chapter Ten: Survey Methodology ........................................................ 119 

Methodology ................................................................................... 120 

Demographics ................................................................................. 124 

Chapter Eleven: Q-Sort Methodology ................................................... 127 

Chapter Twelve: The “File-Drawer” Problem and 

Calculation of Effect Size .................................................................... 129 

Appendix: Selected Literature ............................................................... 133 

Intelligence Tools and Techniques ................................................. 133 

Cognitive Processes and Intelligence ............................................. 134 

Tools and Techniques as Cognitive Processes ............................... 134 

Intelligence Analysis as Individual Cognitive Process ................... 135 

Error ................................................................................................ 135 

Language and Cognition ................................................................. 136 

Bibliography............................................................................................ 139 

Published Sources ........................................................................... 139 

Web resources ................................................................................. 157 

Afterword by Joseph Hayes ................................................................... 159 

The Author .............................................................................................. 161 

ix

FOREWORD 

Gregory F. Treverton 

It is a rare season when the intelligence story in the news concerns intelligence 

analysis, not secret operations abroad. The United States is having such 

a season as it debates whether intelligence failed in the run-up to both September 

11 and the second Iraq war, and so Rob Johnston’s wonderful book is perfectly 

timed to provide the back-story to those headlines. The CIA’s Center 

for the Study of Intelligence is to be commended for having the good sense to 

find Johnston and the courage to support his work, even though his conclusions 

are not what many in the world of intelligence analysis would like to 

hear. 

He reaches those conclusions through the careful procedures of an anthropologist—conducting 

literally hundreds of interviews and observing and participating 

in dozens of work groups in intelligence analysis—and so they 

cannot easily be dismissed as mere opinion, still less as the bitter mutterings 

of those who have lost out in the bureaucratic wars. His findings constitute not 

just a strong indictment of the way American intelligence performs analysis, 

but also, and happily, a guide for how to do better. 

Johnston finds no baseline standard analytic method. Instead, the most common 

practice is to conduct limited brainstorming on the basis of previous analysis, 

thus producing a bias toward confirming earlier views. The validating of 

data is questionable—for instance, the Directorate of Operation’s (DO) “cleaning” 

of spy reports doesn’t permit testing of their validity—reinforcing the tendency 

to look for data that confirms, not refutes, prevailing hypotheses. The 

process is risk averse, with considerable managerial conservatism. There is 

much more emphasis on avoiding error than on imagining surprises. The analytic 

process is driven by current intelligence, especially the CIA’s crown jewel 

analytic product, the President’s Daily Brief (PDB), which might be caricatured 

xi

as “CNN plus secrets.” Johnston doesn’t put it quite that way, but the Intelligence 

Community does more reporting than in-depth analysis. 

None of the analytic agencies knows much about the analytic techniques of 

the others. In all, there tends to be much more emphasis on writing and communication 

skills than on analytic methods. Training is driven more by the 

druthers of individual analysts than by any strategic view of the agencies and 

what they need. Most training is on-the-job. 

Johnston identifies the needs for analysis of at least three different types of 

consumers—cops, spies, and soldiers. The needs of those consumers produce 

at least three distinct types of intelligence—investigative or operational, strategic, 

and tactical. 

The research suggests the need for serious study of analytic methods across 

all three, guided by professional methodologists. Analysts should have many 

more opportunities to do fieldwork abroad. They should also move much 

more often across the agency “stovepipes” they now inhabit. These movements 

would give them a richer sense for how other agencies do analysis. 

Together, the analytic agencies should aim to create “communities of practice,” 

with mentoring, analytic practice groups, and various kinds of on-line 

resources, including forums on methods and problem solving. These communities 

would be linked to a central repository of lessons learned, based on 

after-action post-mortems and more formal reviews of strategic intelligence 

products. These reviews should derive lessons for individuals and for teams 

and should look at roots of errors and failures. Oral and written histories 

would serve as other sources of wherewithal for lessons. These communities 

could also begin to reshape organizations, by rethinking organizational 

designs, developing more formal socialization programs, testing group configurations 

for effectiveness, and doing the same for management and leadership 

practices. 

The agenda Johnston suggests is a daunting one, but it finds echoes in the 

work of small, innovative groups across the Intelligence Community—groups 

more tolerated than sponsored by agency leaders. With the challenge workforce 

demographics poses for the Community—the “gray-green” age distribution, 

which means that large numbers of new analysts will lack mentors as old 

hands retire—also comes the opportunity to refashion methods and organizations 

for doing intelligence analysis. When the finger-pointing in Washington 

subsides, and the time for serious change arrives, there will be no better place 

to start than with Rob Johnston’s fine book. 

xii

INTRODUCTION 

In August 2001, I accepted a Director of Central Intelligence postdoctoral 

research fellowship with the Center for the Study of Intelligence (CSI) at the 

Central Intelligence Agency. The purpose of the fellowship, which was to 

begin in September and last for two years, was to identify and describe conditions 

and variables that negatively affect intelligence analysis. During that 

time, I was to investigate analytic culture, methodology, error, and failure 

within the Intelligence Community using an applied anthropological methodology 

that would include interviews (thus far, there have been 489), direct and 

participant observation, and focus groups. 

I began work on this project four days after the attack of 11 September, and 

its profound effect on the professionals in the Intelligence Community was 

clearly apparent. As a whole, the people I interviewed and observed were 

patriotic without pageantry or fanfare, intelligent, hard working, proud of their 

profession, and angry. They were angry about the attack and that the militant 

Islamic insurgency about which they had been warning policymakers for 

years had murdered close to 3,000 people in the United States itself. There 

was also a sense of guilt that the attack had happened on their watch and that 

they had not been able to stop it. 

Having occurred under the dark shadow of that attack, this study has no 

comparable baseline against which its results could be tested, and it is difficult 

to identify biases that might exist in these data as a result of 11 September. In 

some ways, post-9/11 data may be questionable. For example, angry people 

may have an ax to grind or an agenda to push and may not give the most reliable 

interviews. Yet, in other ways, post-9/11 data may be more accurate. 

When people become angry enough, they tend to blurt out the truth—or, at 

least, their perception of the truth. The people I encountered were, in my judgxiii

INTRODUCTION 

ment, very open and honest; and this, too, may be attributable to 9/11. In any 

case, that event is now part of the culture of the Intelligence Community, and 

that includes whatever consequences or biases resulted from it. 

Background 

The opportunity to do this research presented itself, at least in part, as a 

result of my participation in a multiyear research program on medical error 

and failure for the Defense Advanced Research Projects Agency (DARPA). 1 

The DARPA research focused on team and individual error in minimally invasive 

or laparoscopic surgical procedures. This research revealed that individual 

errors were cognitive rather than purely psychomotor or skill-based. For 

example, some surgeons had trouble navigating three-dimensional anatomical 

space using the existing laparoscopic technology, with the result that these 

surgeons would identify anatomical structures incorrectly and perform a surgical 

procedure on the wrong body part. 

Other individual errors were discovered during the DARPA studies, but, for 

the most part, these were spatial navigation and recognition problems for 

which there were technological solutions. Team errors, unlike individual 

errors, proved to be more challenging. The formal and informal hierarchical 

structures of operating rooms did not lend themselves to certain performance 

interventions. Generally, junior surgical staff and support personnel were not 

willing to confront a senior staff member who was committing, or was about 

to commit, an error. 

The culture of the operating room, coupled with the social and career structure 

of the surgical profession, created barriers to certain kinds of communication. 

For a surgical resident to inform a senior surgeon in front of the entire 

operating room staff that he was about to cut the wrong organ could result in 

career “suicide.” Such a confrontation could have been perceived by the 

senior surgeon as a form of mutiny against his authority and expertise and a 

challenge to the social order of the operating room. Although not universal, 

this taboo is much more common than surgeons would care to admit. Unlike 

individual errors, purely technological solutions were of little value in trying 

to solve team errors in a surgical environment. 

The DARPA surgical research was followed up by a multiyear study of 

individual and team performance of astronauts at the National Aeronautics 

and Space Administration’s (NASA) Johnson Space Center. Results of the 

NASA study, also sponsored by DARPA, were similar to the surgical study 

1 

Rob Johnston, J. Dexter Fletcher and Sunil Bhoyrul, The Use of Virtual Reality to Measure Surgical 

Skill Levels. 

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INTRODUCTION 

with regard to team interactions. Although, on the face of it, teams of astronauts 

were composed of peers, a social distinction nevertheless existed 

between commander, pilots, and mission specialists. 

As with surgery, there was a disincentive for one team member to confront 

or criticize another, even in the face of an impending error. Eighty percent of 

the current astronauts come from the military, which has very specific rules 

regarding confrontations, dissent, and criticism. 2 In addition to the similarities 

in behavior arising from their common backgrounds, the “criticism” taboo 

was continually reinforced throughout the astronaut’s career. Virtually any 

negative comment on an astronaut’s record was sufficient for him or her to be 

assigned to another crew, “washed out” of an upcoming mission and recycled 

through the training program, or, worse still, released from the space program 

altogether. 

Taboos are social markers that prohibit specific behaviors in order to maintain 

and propagate an existing social structure. Generally, they are unwritten 

rules not available to outside observers. Insiders, however, almost always perceive 

them simply as the way things are done, the natural social order of the 

organization. To confront taboos is to confront the social structure of a culture 

or organization. 

I mention the surgical and astronautical studies for a number of reasons. 

Each serves as background for the study of intelligence analysts. Astronauts 

and surgeons have very high performance standards and low error rates. 3 Both 

studies highlight other complex domains that are interested in improving their 

own professional performance. Both studies reveal the need to employ a variety 

of research methods to deal with complicated issues, and they suggest that 

there are lessons to be learned from other domains. Perhaps the most telling 

connection is that, because lives are at stake, surgeons and astronauts experience 

tremendous internal and external social pressure to avoid failure. The 

same often holds for intelligence analysts. 

In addition, surgery and astronautics are highly selective and private disciplines. 

Although their work is not secret, both groups tend to be shielded from 

the outside world: surgeons for reasons of professional selection, training, and 

the fiscal realities of malpractice liability; astronauts because their community 

2 

National Aeronautics and Space Administration, Astronaut Fact Book. 

3 

NASA has launched missions with the shuttle fleet 113 times since 1981 and has experienced 

two catastrophic failures. It is probable that both of those were mechanical/engineering failures 

and not the result of astronaut error. Surgical reporting methods vary from hospital to hospital, 

and it is often difficult to determine the specific causes of morbidity and mortality. One longitudinal 

study of all surgical procedures in one medical center puts the surgical error rates at that center 

between 2.7 percent and 7.5 percent. See Hunter McGuire, Shelton Horsley, David Salter, et al., 

“Measuring and Managing Quality of Surgery: Statistical vs. Incidental Approaches.” 

xv

INTRODUCTION 

is so small and the selection and training processes are so demanding. 4 Intelligence 

analysts share many of these organizational and professional circumstances. 

The Intelligence Community is relatively small, highly selective, and 

largely shielded from public view. For its practitioners, intelligence work is a 

cognitively-demanding and high-risk profession that can lead to public policy 

that strengthens the nation or puts it at greater risk. Because the consequences 

of failure are so great, intelligence professionals continually feel significant 

internal and external pressure to avoid it. One consequence of this pressure is 

that there has been a long-standing bureaucratic resistance to putting in place a 

systematic program for improving analytical performance. According to 

71 percent of the people I interviewed, however, that resistance has diminished 

significantly since September 2001. 

It is not difficult to understand the historical resistance to implementing 

such a performance improvement program. Simply put, a program explicitly 

designed to improve human performance implies that human performance 

needs improving, an allegation that risks considerable political and institutional 

resistance. Not only does performance improvement imply that the system 

is not optimal, the necessary scrutiny of practice and performance would 

require examining sources and methods in detail throughout the Intelligence 

Community. Although this scrutiny would be wholly internal to the community, 

the concept runs counter to a culture of secrecy and compartmentalization. 

The conflict between secrecy, a necessary condition for intelligence, and 

openness, a necessary condition for performance improvement, was a recurring 

theme I observed during this research. Any organization that requires 

secrecy to perform its duties will struggle with and often reject openness, even 

at the expense of efficacy. Despite this, and to their credit, a number of small 

groups within the Intelligence Community have tasked themselves with creating 

formal and informal ties with the nation’s academic, non-profit, and industrial 

communities. In addition, there has been an appreciable increase in the 

use of alternative analyses and open-source materials. 

These efforts alone may not be sufficient to alter the historical culture of 

secrecy, but they do reinforce the idea that the Intelligence Community itself 

has a responsibility to reconsider the relationship between secrecy, openness, 

and efficacy. This is especially true as it relates to the community’s performance 

and the occurrence of errors and failure. External oversight and public 

debate will not solve these issues; the desire to improve the Intelligence Com- 

4 

There are currently 109 active US astronauts and 36 management astronauts. See National Aeronautics 

and Space Administration-Johnson Space Center career astronaut biographies. 

xvi

INTRODUCTION 

munity’s performance needs to come from within. Once the determination has 

been found and the necessary policy guidelines put in place, it is incumbent 

upon the Intelligence Community to find and utilize the internal and external 

resources necessary to create a performance improvement infrastructure. 

Scope 

This project was designed explicitly as an applied research program. In 

many respects, it resembles an assessment of organizational needs and a gap 

analysis, in that it was intended to identify and describe conditions and variables 

that affect intelligence analysis and then to identify needs, specifications, 

and requirements for the development of tools, techniques, and 

procedures to reduce analytic error. Based on these findings, I was to make 

recommendations to improve analytic performance. 

In previous human performance-related research conducted in the military, 

medical, and astronautic fields, I have found in place—especially in the military—a 

large social science literature, an elaborate training doctrine, and welldeveloped 

quantitative and qualitative research programs. In addition to 

research literature and programs, these three disciplines have substantial performance 

improvement programs. This was not the case with the Intelligence 

Community. 

This is not to say that an intelligence literature does not exist but rather that 

the literature that does exist has been focused to a greater extent on case studies 

than on the actual process of intelligence analysis. 5 The vast majority of 

the available literature is about history, international relations, and political 

science. Texts that address analytic methodology do exist, and it is worth noting 

that there are quantitative studies, such as that by Robert Folker, that compare 

the effectiveness of different analytic methods for solving a given 

analytic problem. Folker’s study demonstrates that objective, quantitative, and 

controlled research to determine the effectiveness of analytic methods is possible. 

6 

The literature that deals with the process of intelligence analysis tends to be 

personal and idiosyncratic, reflecting an individualistic approach to problem 

solving. This is not surprising. The Intelligence Community is made up of a 

variety of disciplines, each with its own analytic methodology. The organizational 

assumption has been that, in a multidisciplinary environment, intelli- 

5 

There are exceptions. See the appendix. 

6 

MSgt. Robert D. Folker, Intelligence Analysis in Theater Joint Intelligence Centers. Folker’s 

study contains a methodological flaw in that it does not describe one of the independent variables 

(intuitive method), leaving the dependent variable (test scores) in doubt. 

xvii

INTRODUCTION 

gence analysts would use analytic methods and tools from their own domain 

in order to analyze and solve intelligence problems. When interdisciplinary 

problems have arisen, the organizational assumption has been that a variety of 

analytic methods would be employed, resulting in a “best fit” synthesis. 

This individualistic approach to analysis has resulted in a great variety of 

analytic methods—I identified at least 160 in my research for this paper—but 

it has not led to the development of a standardized analytic doctrine. That is, 

there is no body of research across the Intelligence Community asserting that 

method X is the most effective method for solving case one and that method Y 

is the most effective method for solving case two. 7 

The utility of a standardized analytic doctrine is that it enables an organization 

to determine performance requirements, a standard level of institutional 

expertise, and individual performance metrics for the evaluation and development 

of new analytic methodologies. 8 Ultimately, without such an analytic 

baseline, one cannot assess the effectiveness of any new or proposed analytic 

method, tool, technology, reorganization, or intervention. Without standardized 

analytic doctrine, analysts are left to the rather slow and tedious process 

of trial and error throughout their careers. 

Generally, in research literature, one finds a taxonomy, or matrix, of the 

variables that affect the object under study. Taxonomies help to standardize 

definitions and inform future research by establishing a research “road map.” 

They point out areas of interest and research priorities and help researchers 

place their own research programs in context. In my search of the intelligence 

literature, I found no taxonomy of the variables that affect intelligence analysis. 

Following the literature review, I undertook to develop working definitions 

and a taxonomy in order to systematize the research process. Readers will find 

the working definitions in the first chapter. The second chapter highlights the 

the broader findings and implications of this ethnographic study. Because the 

first two chapters contain many quotes from my interviews and workshops, 

they illustrate the tone and nature of the post-9/11 environment in which I 

worked. 

The taxonomy that grew out of this work was first described in an article for 

the CSI journal, Studies in Intelligence, and is presented here as Chapter 

7 

There is no single Intelligence Community basic analytic training program. There is, however, 

community use of advanced analytic courses at both the CIA University and the Joint Military 

Intelligence College. The Generic Intelligence Training Initiative is a recent attempt to standardize 

certain law enforcement intelligence analysis training programs through a basic law enforcement 

analyst training curriculum. The program has been developed by the Training Advisory 

Council, under the Counterdrug Intelligence Coordinating Group and the Justice Training Center. 

8 

See the appendix. 

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INTRODUCTION 

Three. In addition to the normal journal review process, I circulated a draft of 

the taxonomy among 55 academics and intelligence professionals and incorporated 

their suggestions in a revised version that went to press. This is not to 

assert that the taxonomy is final; the utility of any taxonomy is that it can be 

revised and expanded as new research findings become available. The chapter 

by Dr. Judith Meister Johnston that follows offers an alternative model—more 

complex and possibly more accurate than the traditional intelligence cycle— 

for looking at the dynamics of the intelligence process, in effect the interrelationships 

of many elements of the taxonomy 

The following chapters, prepared by me and other able colleagues, were 

developed around other variables in the taxonomy and offer suggestions for 

improvement in those specific areas. One of them—Chapter Five, on integrating 

methodologists and substantive experts in research teams—also appeared 

in Studies in Intelligence. Chapter Nine contains several broad recommendations, 

including suggestions for further research. 

To the extent possible, I tried to avoid using professional jargon. Even so, 

the reader will still find a number of specific technical terms, and, in those 

cases, I have included their disciplinary definitions as footnotes. 

A Work in Progress 

In some respects, it may seem strange or unusual to have an anthropologist 

perform this type of work rather than an industrial/organizational psychologist 

or some other specialist in professional performance improvement or business 

processes. The common perception of cultural anthropology is one of fieldwork 

among indigenous peoples. Much has changed during the past 40 years, 

however. Today, there are many practitioners and professional associations 

devoted to the application of anthropology and its field methods to practical 

problem-solving in modern or postindustrial society. 9 

It is difficult for any modern anthropological study to escape the legacy of 

Margaret Mead. She looms as large over 20th century anthropology as does 

Sherman Kent over the intelligence profession. Although Franz Boas is arguably 

the father of American anthropology and was Margaret Mead’s mentor, 

hers is the name everyone recognizes and connects to ethnography. 10 Chances 

are, if one has read anthropological texts, one has read Mead. 

9 

The Society for Applied Anthropology and the National Association for the Practice of Anthropology 

section of the American Anthropological Association are the two principal anthropological 

groups. Another group is the Inter-University Seminar on Armed Forces and Society, a 

professional organization representing 700 social science fellows, including practicing anthropologists, 

applying their research methods to issues in the military. 

xix

INTRODUCTION 

I mention Mead not only because my work draws heavily on hers, but also 

because of her impact on the discipline and its direction. She moved from traditional 

cultural anthropological fieldwork in the South Pacific to problemoriented 

applied anthropology during World War II. She was the founder of 

the Institute for Intercultural Studies and a major contributor to the Cold War 

RAND series that attempted to describe the Soviet character. She also pioneered 

many of the research methods that are used in applied anthropology 

today. I mention her work also as an illustrative point. After two years of field 

research in the South Pacific, she wrote at least five books and could possibly 

have written more. 

As I look over the stacks of documentation for this study, it occurs to me 

that, given the various constraints of the fellowship, there is more material 

here than I will be able to address in any one text. There are the notes from 

489 interviews, direct observations, participant observations, and focus 

groups; there are personal letters, e-mail exchanges, and archival material; and 

there are my own notes tracking the progress of the work. Moreover, the fieldwork 

continues. As I write this, I am scheduling more interviews, more observations, 

and yet more fieldwork. 

This text, then, is more a progress report than a final report in any traditional 

sense. It reflects findings and recommendations to date and is in no way 

comprehensive. Finally, based as it is on my own research interests and 

research opportunities, it is but one piece of a much larger puzzle. 

10 

Boas (1858–1942) developed the linguistic and cultural components of ethnology. His most 

notable work was Race, Language, and Culture (1940). 

xx

PART I 

Research Findings 

1

eth•nog•ra•phy\n [F ethnographie, fr. ethno- + -graphie -graphy] (1834) : 

the study and systematic recording of human cultures: also: a descriptive work 

produced from such research. (Merriam Webster’s Collegiate Dictionary, 

Eleventh Edition) 

2

CHAPTER ONE 

Definitions 

Because I conducted human performance–related fieldwork before I came 

to this project, I carried into it a certain amount of experiential bias, or “cognitive 

baggage.” The research findings from those other studies could bias my 

perspective and research approach within the Intelligence Community. For 

example, surgeons and astronauts do not need to deal with intentionally 

deceptive data. Patients are not trying to “hide” their illnesses from surgeons, 

and spacecraft are not thinking adversaries intent on denying astronauts critical 

pieces of information. This one difference may mean that intelligence 

analysis is much more cognitively challenging than the other two cases and 

that the requisite psychomotor skills are significantly less important. In an 

effort to counteract the biases of experience, I will attempt to be explicit about 

my own definitions in this work. 

Working Definitions 

The three main definitions used in this work do not necessarily represent 

definitions derived from the whole of the intelligence literature. Although 

some of the definitions used in this work are based on the Q-sort survey of the 

intelligence literature described later, some are based on the 489 interviews, 

focus groups, and two years of direct and participant observations collected 

during this project. 

3

CHAPTER ONE 

Definition 1: Intelligence is secret state or group activity to understand 

or influence foreign or domestic entities. 

The above definition of intelligence, as used in this text, is a slightly modified 

version of the one that appeared in Michael Warner’s work in a recent 

article in Studies In Intelligence. 1 Warner reviews and synthesizes a number 

of previous attempts to define the discipline of intelligence and comes to the 

conclusion that “Intelligence is secret state activity to understand or influence 

foreign entities.” 

Warner’s synthesis seems to focus on strategic intelligence, but it is also 

logically similar to actionable intelligence (both tactical and operational) 

designed to influence the cognition or behavior of an adversary. 2 This synthesis 

captures most of the elements of actionable intelligence without being too 

restrictive or too open-ended, and those I asked to define the word found its 

elements, in one form or another, to be generally acceptable. The modified 

version proposed here is based on Warner’s definition and the interview and 

observation data collected among the law enforcement elements of the intelligence 

agencies. These elements confront adversaries who are not nation states 

or who may not be foreign entities. With this in mind, I chose to define intelligence 

somewhat more broadly, to include nonstate actors and domestic intelligence 

activities performed within the United States. 

Definition 2: Intelligence analysis is the application of individual 

and collective cognitive methods to weigh data and test hypotheses 

within a secret socio-cultural context. 

This meaning of intelligence analysis was harder to establish, and readers 

will find a more comprehensive review in the following chapter on developing 

an intelligence taxonomy. In short, the literature tends to divide intelligence 

analysis into “how-to” tools and techniques or cognitive processes. This is not 

to say that these items are mutually exclusive; many authors see the tools and 

techniques of analysis as cognitive processes in themselves and are reluctant 

to place them in different categories. Some authors tend to perceive intelligence 

analysis as essentially an individual cognitive process or processes. 3 

My work during this study convinced me of the importance of making 

explicit something that is not well described in the literature, namely, the very 

1 

Michael Warner, “Wanted: A Definition of ‘Intelligence’,” Studies in Intelligence 46, no. 3 

(2002): 15–22. 

2 

US Joint Forces Command, Department of Defense Dictionary of Military and Associated 

Terms. 

3 

The appendix lists literature devoted to each of these areas. 

4

DEFINITIONS 

interactive, dynamic, and social nature of intelligence analysis. The interview 

participants were not asked to define intelligence analysis as such; rather, they 

were asked to describe and explain the process they used to perform analysis. 

The interview data were then triangulated with the direct and participant 

observation data collected during this study. 4 

Despite the seemingly private and psychological nature of analysis as 

defined in the literature, what I found was a great deal of informal, yet purposeful 

collaboration during which individuals began to make sense of raw 

data by negotiating meaning among the historical record, their peers, and their 

supervisors. Here, from the interviews, is a typical description of the analytic 

process: 

When a request comes in from a consumer to answer some question, 

the first thing I do is to read up on the analytic line. [I] check the 

previous publications and the data. Then, I read through the question 

again and find where there are links to previous products. 

When I think I have an answer, I get together with my group and ask 

them what they think. We talk about it for a while and come to some 

consensus on its meaning and the best way to answer the consumer’s 

question. I write it up, pass it around here, and send it out 

for review. 5 

The cognitive element of this basic description, “when I think I have an 

answer,” is a vague impression of the psychological processes that occur during 

analysis. The elements that are not vague are the historical, organizational, 

and social elements of analysis. The analyst checks the previous written products 

that have been given to consumers in the past. That is, the analyst looks 

for the accepted organizational response before generating analytic hypotheses. 

The organizational-historical context is critical to understanding the meaning, 

context, and process of intelligence analysis. There are real organizational 

and political consequences associated with changing official analytic findings 

and releasing them to consumers. The organizational consequences are associated 

with challenging other domain experts (including peers and supervisors). 

The potential political consequences arise when consumers begin to question 

the veracity and consistency of current or previous intelligence reporting. 

Accurate or not, there is a general impression within the analytic community 

4 

In research, triangulation refers to the application of a combination of two or more theories, data 

sources, methods, or investigators to develop a single construct in a study of a single phenomenon. 

5 

Intelligence analyst’s comment during an ethnographic interview. Such quotes are indented and 

italicized in this way throughout the text and will not be further identified; quotes attributable to 

others will be identified as such. 

5

CHAPTER ONE 

that consumers of intelligence products require a static “final say” on a given 

topic in order to generate policy. This sort of organizational-historical context, 

coupled with the impression that consumers must have a final verdict, tends to 

create and reinforce a risk-averse culture. 

Once the organizational context for answering any given question is understood, 

the analyst begins to consider raw data specific to answering the new 

question. In so doing, the analyst runs the risk of confirmation biases. That is, 

instead of generating new hypotheses based solely on raw data and then 

weighing the evidence to confirm or refute those hypotheses, the analyst 

begins looking for evidence to confirm the existing hypothesis, which came 

from previous intelligence products or was inferred during interactions with 

colleagues. The process is reinforced socially as the analyst discusses a new 

finding with group members and superiors, often the very people who collaborated 

in producing the previous intelligence products. Similarly, those who 

review the product may have been the reviewers who passed on the analyst’s 

previous efforts. 

This is not to say that the existing intelligence products are necessarily inaccurate. 

In fact, they are very often accurate. This is merely meant to point out 

that risk aversion, organizational-historical context, and socialization are all 

part of the analytic process. One cannot separate the cognitive aspects of intelligence 

analysis from its cultural context. 

Definition 3: Intelligence errors are factual inaccuracies in analysis 

resulting from poor or missing data; intelligence failure is systemic 

organizational surprise resulting from incorrect, missing, 

discarded, or inadequate hypotheses. 

During interviews, participants were asked to explain their understanding of 

the terms intelligence error and intelligence failure. There was little consensus 

regarding the definitions of error and failure within the Intelligence Community 

or within the larger interview sample. Here are some sample responses: 

I don’t know what they mean. 

There are no such things. There’s only policy failure. 

You report what you know, and, if you don’t know something, then it 

isn’t error or failure. It’s just missing information. 

Failure is forecasting the wrong thing. 

Failure is reporting the wrong thing. 

Error is forecasting the wrong thing. 

6

DEFINITIONS 

Error is reporting the wrong thing. 

A failure is something catastrophic, and an error is just a mistake. 

Error is about facts; failure is about surprise. 

Error is when nobody notices, and failure is when everybody 

notices. 

Some responses disavowed the existence of intelligence error and failure; 

some placed the terms in the broader context of policy and decisionmaking; 

some interchanged the two terms at random; some defined the terms according 

to their outcomes or consequences. Despite the variability of the 

responses, two trends emerged: novice analysts tended to worry about being 

factually inaccurate; senior analysts, managers, and consumers, tended to 

worry about being surprised. Often, participants’ responses were not definitions 

at all but statements meant to represent familiar historical examples: 

The attack on Pearl Harbor. 

The Chinese sending combat troops into Korea. 

The Tet Offensive. 

The Soviet invasion of Afghanistan. 

The collapse of the Soviet Union. 

The Indian nuclear test. 

September Eleventh. 

The danger of defining by example is that each case is contextually unique 

and can be argued ad infinitum. What is important about these examples as a 

whole is that they all indicate one central and recurring theme. Specifically, all 

these examples signify surprise—in some cases, intelligence surprise; in other 

cases, military, civil, and political surprise. Even if the Intelligence Community 

itself was not surprised by one of these events, it was unable to convince 

the military, civil, and political consumers of intelligence that these events 

might occur; in which case, the failure was one of communication and persuasion. 

When I began this study, my own definition of error and failure derived 

from the psychological and cognitive disciplines. Specifically, I took it that 

human error and failure are related to measures of cognitive and psychomotor 

accuracy, commission of error being at one end of the accuracy scale and 

omission or not performing the correct action being at the other. 6 

6 

See Appendix A for a list of literature on error. 

7

CHAPTER ONE 

During the interviews for this study, I soon found that the psychological 

definition was insufficient. The psychological definition took into account the 

cognitive and psychomotor components of task-structure, time-to-task, and 

accuracy-of-task as measures of errors and error rates, but it did not fully take 

into account the notion of surprise. 7 Surprise is the occurrence of something 

unexpected or unanticipated. It is not precisely commission or omission; it 

indicates, rather, the absence of contravening cognitive processes. Measures 

of accuracy may account for factual errors in the intelligence domain, but 

measures of accuracy are insufficient to account for surprise events and intelligence 

failure. 

To put this in context, an analyst, while accounting successfully for an 

adversary’s capability, may misjudge that adversary’s intention, not because 

of what is cognitively available, but because of what is cognitively absent. 

The failure to determine an adversary’s intention may simply be the result of 

missing information or, just as likely, it may be the result of missing hypotheses 

or mental models about an adversary’s potential behavior. 

7 

Sociological definitions are more akin to the definitions proposed in this study. Failure can occur 

due to system complexity and missing data as well as through the accumulation of error. See 

Charles Perrow, Normal Accidents. Living with High Risk Technologies. I’d like to thank Dr. Perrow 

for his assistance with this work. 

8

CHAPTER TWO 

Findings 

Scientific knowledge, like language, is intrinsically the common 

property of a group or else nothing at all. To understand it we shall 

need to know the special characteristics of the groups that create 

and use it. 

Thomas Kuhn 1 

The more we learn about the world, and the deeper our learning, the 

more conscious, specific, and articulate will be our knowledge of 

what we do not know. 

Karl Popper 2 

The purpose of this research was to identify and describe conditions and 

variables that negatively affect intelligence analysis, to develop relevant and 

testable theory based on these findings, and to identify areas in which strategies 

to improve performance may be effective. Although there has recently 

been a great deal of concern that intelligence error and failure rates are inordinately 

high, in all likelihood, these rates are similar to those of other complex 

socio-cognitive domains, such as analysis of financial markets. The significant 

differences are that other complex domains employ systematic performance 

1 

Philosopher of science Thomas Kuhn described the now-common concept of paradigm shifts in 

scientific revolutions. He posited that paradigm shifts are tied to cultural and social constructionist 

models, such as Vygotsky’s (See footnote 22 in Chapter Three). Thomas Kuhn, The Structure 

of Scientific Revolutions. 

2 

Karl Popper was one of the 20th century’s pre-eminent philosophers of science. Karl Popper, 

Conjectures and Refutations: The Growth of Scientific Knowledge. 

9

CHAPTER TWO 

improvement infrastructures and that the consequences of intelligence error 

and failure are disproportionately high in comparison with other domains. 

It is evident from the literature that intelligence organizations recognize the 

need to improve their performance and that it is possible to make the domain 

of intelligence analysis into a coherent scientific discipline. The first step in 

this transition is to identify and describe performance gaps. 3 Once gaps have 

been identified, it will be possible to introduce performance improvement 

methods systematically and to measure the effectiveness of the results. This 

work is intended to further research toward creating intelligence organizations 

that are more effective. 

The Problem of Bias 

Although a researcher might pretend to be neutral and unbiased in presenting 

his findings and conclusions, personal biases can creep into a finished 

product. The methods ethnographers employ to collect raw data and the use of 

interpretational analysis to extract meaning and generate theory virtually guarantee 

it. In my view, one should be candid about this possibility. I noted in 

Chapter One that ethnographers bring a certain amount of experiential baggage 

to their work, myself included. At this point, before discussing analytical 

difficulties and problems I identified during my research, I want to make the 

readers aware of an additional personal bias that has developed from observing 

the Intelligence Community. 

During my research, I developed a great deal of empathy for individual analysts 

and the problems they face in trying to perform their jobs. The reason for 

this is straightforward and something every anthropologist recognizes. It is 

part of the process that anthropologists reach a point where they can modify 

their own identity in order to gain insight into a different culture. The risk is 

that empathy and identity modification will induce the researcher to “go 

native” and produce bias in his findings. 

Although I may empathize with analysts personally, it is critical for theory 

development to avoid parroting the views, kudos, or complaints of individual 

analysts, who may or may not be dissatisfied with their unique professional 

experience. In order to counteract the empathy bias, I employed multiple data 

collection techniques and then used those data to refute or confirm each categorical 

finding. Triangulation is not an infallible system, however, and the 

reader is advised to approach these findings with both a critical eye and the 

3 

Performance gaps are the difference or distance between ideal (perfect) organizational performance 

and actual organizational performance. In this case, ideal performance includes complete 

data sets, reportorial accuracy, and the ability to avoid strategic, operational, and tactical surprise. 

10

FINDINGS 

foreknowledge that this researcher has a number of personal and professional 

biases. 4 

Finding: Secrecy Versus Efficacy 

Secrecy and efficacy conflict. Secrecy interferes with analytic effectiveness 

by limiting access to information and sources that may be necessary for accurate 

or predictive analysis. In turn, openness interferes with security by 

degrading the value of information resources and by revealing specific 

sources and methods. 

Perfect secrecy would ultimately be unproductive, because it would 

restrict information to one mind or to a very small group of minds. Limiting 

available resources in this way would produce organizational failure in 

competition with resources available to a large and diverse group of adversaries. 

Perfect openness would also lead to organizational failure, because, 

with full access to all information, there would never be an instance of 

advantage for any one group over any other group. In addition, perfect 

openness would result in adversaries being aware they are under observation 

and could lead them to alter their behavior to deceive the observer if 

they so desired. 5 

Between these two extremes, there is some notional point where secrecy 

and openness converge to create an optimal performance tradeoff. My perception 

is that, within the Intelligence Community, more organizational emphasis 

is placed on secrecy than on effectiveness. It is important, in my view, that 

there be a voice in favor of openness to counterbalance the many voices 

whose sole or primary responsibility is the advocacy and maintenance of 

4 

Throughout the project, my data collection method consisted of written field notes. Anthropologists 

traditionally include specific detail from participant input or direct observation. Usually, this is 

in the form of precise descriptions of the actual behavior of participants and transcripts of their verbal 

interactions. It is also standard practice in field work to capture these data, and the data from the 

interviews and focus groups, on audio- or videotape. These practices were not followed in this particular 

case for two reasons: first, the nature of my work was not to document actual practices and 

procedures; rather, it was to derive categories of variables and individual variables in order to create 

a taxonomy, and to use the prototype taxonomy to structure the interactions; second, the nature of 

intelligence work and the environment in which it occurs, as well as its professional practitioners, 

require that certain data be restricted. 

5 

This has been demonstrated in the psychological literature and is referred to as the Hawthorne 

Effect. Derived from research that began with an experimental program at Western Electric’s 

Hawthorne Works conducted between 1927 and 1930, the Hawthorne Theory, broadly interpreted, 

states that the behavior of subjects changes when they are aware of being observed. See 

Fritz J. Roethlisberger and William J. Dickson, Management and the worker; Elton Mayo, The 

Human Problems of an Industrial Civilization. 

11

CHAPTER TWO 

Secrecy vs. Efficacy 

secrecy. I believe this secrecy-efficacy conflict can be stated as a theory, along 

the following lines. 6 

The more open the system (where zero is perfect information access and 

sharing on the X axis secrecy scale [as shown on the above graph]), the more 

access an analyst has to all sources of information within the Intelligence 

Community regarding an adversary. In addition, this openness encourages 

interorganizational communication, interaction, and sharing of information 

among analysts and increases the likelihood that an analyst will be more efficient 

(in this case the Y axis efficiency scale) and therefore effective or accurate 

in his or her assessment of a situation. 

Conversely, counter-intelligence is negatively affected by zero-level 

secrecy and perfect openness. The less open or more compartmentalized the 

system, the more efficient and effective are counterintelligence activities. 

Notionally, the two curves would meet somewhere in the tradeoff between 

efficiency and secrecy. Where they meet would depend on program goals and 

a clear definition of starting points and end-states. 

The notional set of curves above illustrates the tradeoff between system 

efficiency and system secrecy and the effect that the tradeoff has on performance 

effectiveness, both positive and negative. In this case, the starting and 

ending points of effectiveness for analysis and for counterintelligence are 

arbitrary and could be positioned anywhere along a continuum between zero 

6 

I would like to credit and thank Matthew Johnson at the Institute for Defense Analyses for his 

help in formulating this theory. 

12

FINDINGS 

and ten. In this theory, analytic efficiency and effectiveness are purely functions 

of system openness and do not take into account analytic methods or personnel. 

This theory will require additional refinement, and it may or may not be 

represented by a tradeoff curve like the one proposed here. The theory will 

also require numerous controlled quantitative experiments to test its explanatory 

power. 

Finding: Time Constraints 

The work itself is a 24-hour-a-day job, but it never seems like I have 

any time to actually analyze anything when I’m at my desk. I spend 

most of my time reading daily traffic, answering e-mail, coordinating 

papers with everybody, and writing. Mostly I read and write, but 

when the workday is over, I go home and think. It isn’t like I can 

turn off my brain. So, I guess I do most of my real analysis on my 

own time. 

The majority of the analysts interviewed indicated that time was one of 

their greatest constraints at work. This comment triangulated with the findings 

from direct and participant observation. In addition, analysts indicated that 

there has been a communitywide shift toward focusing on short-term issues or 

problem solving, thereby addressing the immediate needs of intelligence consumers. 

This shift in product focus, coupled with a growth in available allsource 

raw intelligence, has resulted in a change in the pace of analytic production. 

In order to generate the daily products, analysts have had to change 

the way they go about doing their work. 

I haven’t been doing this very long, but I wish I had been a journalism 

major instead of poli-sci. The pace is excruciating. 

I don’t get much sleep. It’s like cramming for finals, except we do it 

every day. 

Everything I do is reactive. I don’t have time to work my subject. 

We’re not pro-active here. 

I’m so busy putting out today’s fires, I don’t have any time to think 

about what kind of catastrophe is in store for me a month from now. 

About 15 years ago, I used to have 60 percent of my time available 

for long-term products. Now, it’s between 20 and 25 percent. 

I probably have about 30 percent of my time for self-initiated products. 

13

CHAPTER TWO 

You know, someday somebody is bound to notice that velocity isn’t 

a substitute for quality. We’ve gotten rid of the real analytic products 

that we use to make, and now we just report on current events. 

Not all analysts indicated that time constraints and information load had a 

negative effect on their performance. A minority indicated that there was sufficient 

time to perform analytic duties and prepare analytic products. 

This is a tactical shop. It’s all we do. Current reporting is our job. 

I work a slow desk. I have plenty of time for self-initiated products 

—maybe 60 percent or more. 

I multitask pretty well. I don’t really experience a time-crunch. 

Maybe I just process better than other people, but I don’t really feel 

pressed for time. Besides, I’d rather be at a hot desk than at a cold 

desk. 

Analytic supervisors were more evenly mixed in their opinions about time 

constraints. A slight majority of the managers interviewed said time constraints 

had negative effects on the work environment, work processes, and the 

morale of their staff. A majority of them also put analytic time constraints in a 

larger context of policy making. They indicated that the decision-cycle of policymakers 

was 24 hours a day and that their responsibility was to support that 

decision cycle with current intelligence. 

In discussing their perceptions of consumer demand, the managers’ views 

of the nature of those demands were mixed. 

I want my analysts to produce long-term products. I want them 

thinking through their subjects. The decision makers want wellthought-out 

products, not just daily briefs. 

Our customers want current production. They never complain about 

the daily products and, frankly, I doubt they have time to read the 

longer stuff. 

My consumers like the bigger pieces. They like having the context 

and broader picture. They don’t want to be spoon fed. 

I’ve never had a customer tell me they want more to read. 

Our customers want to avoid surprise. As long as we keep them 

from being surprised, I don’t care if we do daily or long-term production. 

I don’t think they care either. 

14

FINDINGS 

Finding: Focus on Current Production 

The present daily production cycle and the focus on current intelligence 

also affect group interactions and the analytic process. 

Group Interactions: 

It doesn’t matter if I’m writing a piece myself or if I’m coordinating 

a piece with some group. We don’t sit around and test hypotheses, 

because we’re too busy writing. We’ve got serious deadlines here. 

If, by group analysis, you mean the senior expert in the room tells 

everybody what he thinks, and then we generally agree so that we 

can get back to our own deadlines, then, sure, there’s a group process. 

We used to have groups that did current reporting and different 

groups that did longer term products. We still have some of that, but 

it is very limited. I couldn’t say what happened exactly, but we’re all 

doing current production now. 

The Analytic Process: 

People seem to have confused writing with analyzing. They figure 

that if you just go through the mechanics of writing something, then 

you must have analyzed it. I don’t know about everybody else, but it 

doesn’t work that way for me. I need time to think through the problem. 

Our products have become so specific, so tactical even, that our 

thinking has become tactical. We’re losing our strategic edge, 

because we’re so focused on today’s issues. 

Alternative analysis is a nice concept, but I don’t have the time to do 

it. I’ve got to keep up with the daily traffic. 

I use several analytic techniques that are relatively fast. Scenario 

development, red teams, competing hypotheses, they’re all too time 

consuming. 

We’ve got Bayesian tools, simulations, all kinds of advanced methods, 

but when am I supposed to do any of that? It takes all my time 

to keep up with the daily reporting as it is. 

I don’t have time to worry about formal analytic methods. I’ve got 

my own system. It’s more intuitive and a lot faster. 

15

CHAPTER TWO 

Finding: Rewards and Incentives 

The shift in the analytic production cycle is not only reflected in the products 

and processes but also in the way analysts perceive the system by which 

intelligence organizations reward and promote employees. Employees see 

their opportunities for promotion as being tied directly to the number of daily 

products they generate and the amount of social capital or direct consumer 

influence they amass, most often when their work is recognized by senior policymakers. 

7 

In any given week, I could devote about 20 percent of my time to 

longer think pieces, but why should I? You can write all the think 

pieces you want, but, if you don’t write for the daily briefs, you 

aren’t going to move into management. These days the only thing 

that matters is getting to the customers. 

If I write a 12-page self-directed piece that goes out as a community 

product, and somebody else writes one paragraph with two bullet 

points that goes into a daily brief, the guy who got in the daily brief 

is going to get the recognition. Why waste my time with the big 

products? 

It isn’t really official policy, but the reality is that sheer production 

equals promotion. People talk about quality, but, in the end, the 

only measurable thing is quantity. 

Our group has a “team award” of 5,000 bucks. Last year, they gave 

it to the one guy who published the most. I’m not sure how that one 

guy won a “team award,” but there you go. 

Technically, I have four bosses. The only thing that seems to keep 

them all happy is volume. It’s like piece work. 

Quality? How do you measure quality? Quantity—now that’s something 

you can count. 

Promotion is based on production—pure and simple. 

In sum, aside from specific tactical groups, staff positions that generate limited 

social capital, and individual cognitive differences, there is a majority 

sentiment among the analysts interviewed that the combination of a shorter 

7 

Social capital refers to the set of norms, social networks, and organizations through which people 

gain access to power, resources, and reciprocity and through which decisionmaking and policy 

creation occur. In other words, whom you know is just as important as what you know. Pierre 

Bourdieu, “The Forms of Capital”; Robert Putnam, “The Prosperous Community” and Bowling 

Alone. See also the empirical work on social capital summarized in Tine Feldman and Susan 

Assaf, Social Capital: Conceptual Frameworks and Empirical Evidence. 

16

FINDINGS 

production cycle, information load, a shift in product focus, and organizational 

norms regarding promotion have had an impact on analytic work and 

intelligence analysis itself. 

Finding: “Tradecraft” Versus Scientific Methodology 

Human beings do not live in the objective world alone, nor alone in 

the world of social activity as ordinarily understood, but are very 

much at the mercy of the particular language which has become the 

medium of expression for their society…The fact of the matter is that 

the “real world” is to a large extent unconsciously built upon the 

language habits of the group…We see and hear and otherwise experience 

very largely as we do because the language habits of our 

community predispose certain choices of interpretation. 

Edward Sapir 8 

The Intelligence Community, in its culture and mythos and in its literature, 

tends to focus on intelligence operations rather than on intelligence analysis. 

Open literature about the community certainly does so. Along with time constraints 

and the analytic production cycle, the private and public focus on 

operations has had an effect on intelligence analysts and analytic methodology. 

The principal effect is the spread of the concept of “tradecraft” within the 

analytic community. 

Community members quite often used the word “tradecraft” to describe 

intelligence analysis during the interviews, observations, training programs, 

workshops, and actual analytic tasks that I performed for this study. Analysts, 

managers, instructors, and academic researchers employed the word “tradecraft” 

as a catchall for the often-idiosyncratic methods and techniques 

required to perform analysis. Although the intelligence literature often refers 

to tradecraft, the works tend to be a collection of suggestions and tips for writing 

and communicating with co-workers, supervisors, and consumers instead 

of focusing on a thorough examination of the analytic process and techniques. 

The notion that intelligence operations involve tradecraft, which I define as 

practiced skill in a trade or art, may be appropriate, but the analytic community’s 

adoption of the concept to describe analysis and analytic methods is not. 

The obvious logical flaw with adopting the idea of tradecraft as a standard of 

practice for analytic methodology is that, ultimately, analysis is neither craft 

nor art. Analysis, I contend, is part of a scientific process. This is an important 

8 

Edward Sapir is best known for the Sapir-Whorf hypothesis, which asserts linguistic/cognitive 

relativity (language and thought are inseparable; therefore, different languages mean different 

ways of thinking). Edward Sapir, Language. 

17

CHAPTER TWO 

distinction, for language is a key variable in anthropology and often reveals a 

great deal about the cognition and culture of a community of interest. 9 

The adoption by members of the analytic community of an inappropriate 

term for the processes and methods employed in their professional lives 

obfuscates and complicates the reality of their work. The adoption of the 

word “tradecraft” demonstrates the analytic community’s need to create a professional 

identity separate and unique from other disciplines but tied directly 

to the perceived prestige and cachet of intelligence operations. Adopting 

“tradecraft” as a term of reference for explaining work practices and as a professional 

identity marker may seem trivial. Yet the term, and its effect on the 

community, has unanticipated consequences. 

Tradecraft purposefully implies a mysterious process learned only by the 

initiated and acquired only through the elaborate rituals of professional indoctrination. 

It also implies that the methods and techniques of analysis are informal, 

idiosyncratic, unverifiable, and perhaps even unexplainable. “Good” 

methods are simply those that survive, and then are passed on by “good” analysts 

to novice analysts. Unfortunately, “good” in both instances is not an 

objective measure. That is, there is no formal system for measuring and tracking 

the validity or reliability of analytic methods, because they are both perceived 

and employed within the context of idiosyncratic tradecraft. When 

asked to describe the analytic process, analysts responded in a variety of ways. 

First, I figure out what I know and what I don’t know about some 

situation. Then, I look for information to fill the gap. 

I have a model of the situation in my head. Whenever something new 

comes in, I see if it fits with the model. If it does, I add it to the 

model; if it doesn’t, I try to figure out why. 

I’ve found a system that lets me keep up. I just look for anomalies. 

When I see any novel data, then I worry. 

I’m always looking for anything strange or out of place. Then, I 

source it to see if it is meaningful. 

The current data ought to fit a certain pattern. If it doesn’t, I know 

something is wrong. 

First, I print the daily traffic I’m concerned with; then I lay out all 

of the relevant stuff in front of me on my desk or the floor; then I 

start looking for threads. 

9 

The literature on this subject is extensive. For a representative list, see the appendix. 

18

FINDINGS 

I’m looking for links and patterns. Once I figure out the pattern, I 

can figure out where to look next. 

I use patterns. If things start happening, out of the ordinary things, I 

pay attention to them. 

I try to build patterns out of the data. It helps me predict what will 

happen next. 

I come up with a few scenarios and see what the evidence supports. 

I look for data that are diagnostic: some piece of evidence that rules 

out certain possibilities. 

I try to weigh the evidence to see which scenario it supports. 

Although anomaly-detection, pattern-recognition, and weighing data may 

appear to be idiosyncratic tradecraft based on individual expertise and cognitive 

skills, these methods can be formalized and replicated if the operating 

parameters, variables, and rules of evidence are made explicit. 10 This is to say 

that intelligence analysis can be reconstructed in the context of a scientific 

method, which is merely an articulated, formal process by which scientists, 

collectively and over time, endeavor to put together a reliable, consistent, and 

nonarbitrary representation of some phenomena. Broadly, the steps include: 

• observation and description of phenomena; 

• formulation of hypotheses to explain phenomena; 

• testing of hypotheses by independent experts; 

• refutation or confirmation of hypotheses. 

These steps do not suggest that any specific scientific methodology results 

in what is ultimately the truth, rather that scientific methods are merely formal 

processes used to describe phenomena, make predictions, and determine 

which hypothesis best explains those phenomena. The principal value of any 

type of methodological formalism is that it allows other researchers to test the 

validity and reliability of the findings of any other researcher by making 

explicit, and therefore replicable, the means by which anyone reaches a specific 

conclusion. 11 

The idea that intelligence analysis is a collection of scientific methods 

encounters some resistance in the Intelligence Community. The interview data 

analyzed in this study highlight many subtle—and not so subtle—prejudices 

10 

A corollary to these methods can be found in the practice of radiologists. See Chapter Five for 

more on expertise. 

19

CHAPTER TWO 

that analysis is not a science. That is, it is an art or craft in which one can 

attain skill but not a formal discipline with tested and validated methodology. 

What we do is more art and experience than anything else. 

Science is too formal. We can’t actually run experiments here. 

How would you actually test a hypothesis in intelligence? 

Science is what you do in a lab. 

We’re not scientists; we’re analysts. We don’t generate the data. 

We don’t worry too much about theory; we worry about the facts. 

In my discipline, I might be a scientist, but, in intelligence, I am a 

practitioner. 

I use science for my area, but I don’t think intelligence analysis is 

science. 

As long as intelligence analysis continues to be tradecraft, it will remain a 

mystery. The quality of any tradecraft depends on the innate cognitive capabilities 

of the individual and the good fortune one has in finding a mentor who 

has discovered, through many years of trial and error, unique methods that 

seem to be effective. This process of trial and error is, in general, similar to 

any scientific process, except that the lessons learned in tradecraft, unlike 

those of other disciplines, often occur without being captured, tested, or validated. 

In an oral tradition, individual tradecraft methods are passed on by means 

of apprenticeship. The consequence for any culture tied to an oral tradition is 

the loss of important knowledge that occurs with the loss of practitioners. In 

organizations, the retirement of experts and innovators leads to the loss of that 

expertise and innovation, unless there is some formal written and educational 

system to keep that knowledge alive. 12 

The data collected through both interviews and observation indicated that 

there were, in fact, general methods that could be formalized and that this process 

would then lead to the development of intelligence analysis as a scientific 

discipline. The principal difficulty lies not in developing the methods them- 

11 

Rather than engage in the longstanding and ongoing debate in the academic community about 

what is and what is not science or a scientific method, suffice it to say that any scientific method 

needs to be explicit, replicable, and refutable. The literature surrounding this debate is voluminous. 

The philosophy of science, logic, language, and epistemology has taken this debate in a 

number of directions. There is, however, a general theme that replication is a key ingredient to 

any scientific method. 

12 

See section on Endangered Languages in Barbara Grimes, ed., Ethnologue. 14th ed. 

20

FINDINGS 

selves, but in articulating those methods for the purpose of testing and validating 

them and then testing their effectiveness throughout the community. In the 

long view, developing the science of intelligence analysis is easy; what is difficult 

is changing the perception of the analytic practitioners and managers 

and, in turn, modifying the culture of tradecraft. 

Finding: Confirmation Bias, Norms, and Taboos 

Organization is key, because it sets up relationships among people 

through allocation and control of resources and rewards. It draws 

on tactical power to monopolize or parcel out liens and claims, to 

channel action into certain pathways while interdicting the flow of 

action into others. Some things become possible and likely; others 

are rendered unlikely. 

Eric Wolf 13 

Time constraints affect both the general analytic production cycle and analytic 

methodology by contributing to and exacerbating cognitive biases. 

Although there are any number of cognitive biases to which the human mind 

is susceptible, one in particular became evident during the triangulation phase 

and interpretive analysis of the interview and observation data of this study. 

The cognitive bias identified most often was confirmation bias, which is the 

tendency of individuals to select evidence that supports rather than refutes a 

given hypothesis. 14 

Although the psychological mechanism by which confirmation bias occurs 

is in debate, confirmatory behavior is a consistent finding throughout the 

experimental psychology and cognitive science literature. Rather than focus 

on the mechanism and nomenclature, the term “confirmation bias” is used in 

this work as a description of confirmatory behavior. This behavior was 

13 

Eric Wolf was an anthropologist who focused on power, social structures, and the third world. 

His work on power and the lives of peasants is considered a modern anthropological classic. Eric 

Wolf, Pathways of Power. 

14 

There is a fair amount of disagreement in the psychological literature regarding the mechanism 

by which an individual displays confirmatory behavior. Some researchers attribute it to motivational 

factors, for example, a desire to maintain respect within a group. Other researchers attribute 

it to selectivity factors, an unconscious cognitive selection of data that confirms the current status 

quo. Some researchers attribute it to social factors, a subspecies of groupthink (see Irving Janis, 

Groupthink). Still others ascribe it to a misapplication of heuristics, whereby an individual learns 

a set of rules that solves one problem and then begins using that same set of rules to try to solve 

other types of problems. Although the literature is extensive, Karl Popper’s The Logic of Scientific 

Discovery provides a foundation for understanding the issue. Jonathan Evans’ Bias in Human 

Reasoning: Causes and Consequences is still a useful and concise summary of the research 

related to confirmation bias. 

21

CHAPTER TWO 

described by participants during the interviews and observed during direct and 

participant observations throughout the fieldwork. 

Analysts were asked to describe the work processes they employed to 

answer questions, solve problems, describe and explain phenomena, make 

forecasts, and develop intelligence products. The process they described 

began with an examination of previous analytic products developed by their 

organization in order to establish a baseline from which they could build their 

own analysis. 

When a request comes in from a consumer to answer some question, 

the first thing I do is to read up on the analytic line. 

The first thing I do is check the pervious publications, and then I 

sort through the current traffic. 

I’ve looked at our previous products, and I’ve got a good idea of the 

pattern; so, when I sort through the traffic, I know what I’m trying 

to find. 

I try to keep up with all the products that come out of our area, so I 

know where to start my piece. 

A literature search is often the first step in any research endeavor. The utility 

of this practice is not merely to define and understand the current state of 

research in the field but also to determine major controversies and divergences 

of opinion. Trying to discern controversies and divergence in intelligence 

products is often difficult, because some of them—national intelligence estimates 

(NIE), in particular—are specifically designed to produce a corporate 

consensus for an audience of high-level policymakers. 

These products can and do include divergent opinions, in the form of footnotes, 

but these tend to indicate inter-, rather than intra-, organizational differences. 

Dissenting footnotes are products of the coordination process, the result 

of an inability on the part of one or several community organizations to convince 

the others of a particular point of view. Not surprisingly, the least probable 

opinion is often the hardest to defend, whereas the most probable opinion 

is the easiest to support. 

The literature search approach may promote a logical consistency among 

analytic products, but it has the unintended consequence of imposing on the 

analyst using it a preexisting mental model of the phenomena in question. The 

existing analytic products describe, implicitly or explicitly, a set of working 

hypotheses that an analyst may wish to reflect in his or her own work. Of 

course, these existing hypotheses are rarely tested each time they are incorporated 

into new products. What tends to occur is that the analyst looks for current 

data that confirms the existing organizational opinion or the opinion that 

22

FINDINGS 

seems most probable and, consequently, is easiest to support. As this strategy 

is also the most time-efficient technique, it reduces the time constraints associated 

with the daily production cycle. 

This tendency to search for confirmatory data is not necessarily a conscious 

choice; rather, it is the result of accepting an existing set of hypotheses, developing 

a mental model based on previous corporate products, and then trying to 

augment that model with current data in order to support the existing hypotheses. 

Although motivational and heuristic factors and a tendency toward 

“groupthink” might contribute to confirmatory behavior in intelligence analysis, 

my observations and interviews during this study suggest that the predominant 

influence is selectivity bias in order to maintain a corporate judgment. 

The maintenance of a corporate judgment is a pervasive and often-unstated 

norm in the Intelligence Community, and the taboo against changing the corporate 

product line contributes to confirmation biases. Once any intelligence 

agency has given its official opinion to policymakers, there exists a taboo 

about reversing or significantly changing the official or corporate position to 

avoid the loss of status, trust, or respect. Often, policymakers perceive a 

change in judgment as though the original opinion was wrong, and, although 

unstated, there are significant internal and external social pressures and consequences 

associated with being perceived as incorrect. 

An analyst can change an opinion based on new information or by revisiting 

old information with a new hypothesis; in so doing, however, he or she 

perceives a loss of trust and respect among those with whom the original judgment 

was shared. Along with this perceived loss of trust, the analyst senses a 

loss of social capital, or power, within his or her group. 15 

It is even more difficult for an intelligence agency to change its official 

position once it has made its judgments known to those outside of the organization. 

There is a sense that changing the official product line will be seen outside 

of its context—the acquisition of new information, for instance—and that 

it will be perceived by the policymakers as an example of incompetence or, at 

least, of poor performance on the part of the intelligence agency. 

This perception then carries with it the threat of a loss in status, funding, 

and access to policymakers, all of which would have a detrimental effect on 

the ability of the intelligence agency to perform its functions. In short, it 

serves the interest of the intelligence agency to be perceived as decisive 

15 

Reciprocity in this case has to do with information, judgment, and trust. The classic anthropological 

text on social reciprocity and trust within and between groups is Marcel Mauss’s The Gift. 

Originally published in 1950 and based in part on the work of his uncle and mentor, Emile 

Durkheim, Mauss’s work (Essai sur le Don in its French version) lays the foundation for his contention 

that reciprocity is the key to understanding the modern concept of social capital. 

23

CHAPTER TWO 

instead of academic and contradictory, and that message is transmitted to the 

analysts. In response to the organizational norm, the analyst is inclined to 

work the product line rather than change it. 

Our products are company products, not individual products. When 

you publish something here, it’s the official voice. It’s important for 

us to speak with one voice. 

It doesn’t do us any good if people think we can’t make up our mind. 

Access matters; if people think you don’t know what you’re talking 

about, then they stop seeing you. 

We already briefed one thing. I can’t go in there and change it now. 

We’ll look like idiots. 

When I was new, I wrote a piece that disagreed with our line. Let’s 

just say, I’m more careful about that now. 

Another organizational norm that contributes to confirmation bias in the 

Intelligence Community is the selection and weighing of data according to 

classification. Secrets carry the imprimatur of the organization and, in turn, 

have more face validity than information collected through open sources. 16 

Most analysts indicated that they considered “secret” data collected by 

covert means to be more important or meaningful than “open” or unclassified 

data. Analysts said that they rely on open sources to help fill in missing pieces 

of their mental models but that they test the model’s validity with secret information. 

Choosing to rely on classified data as more meaningful to problem 

solving and as a tool for testing the validity of their hypotheses serves to exacerbate 

the confirmation bias. 

I’m an all-source analyst, so I use whatever I can get my hands on; 

but, if the traffic comes from operations, I tend to pay more attention 

to it than to information in the open literature. 

There is something special about the word “secret” in my business. 

It says that it must be important because people had to go and get it 

rather than its just showing up in the news. We tend to weigh classified 

material as more important than other sources. 

16 

In research methodology, face validity is the concept that a measurement instrument appears or 

seems to measure what it is actually intended to measure and requires no theoretical supporting 

material. In contrast, content validity depends on the content of the domain and established theories 

to determine its measures of validity. See David Brinberg and Joseph McGrath, Validity and 

the Research Process; Edward Carmines and Richard Zeller, Reliability and Validity Assessment; 

Jerome Kirk and Marc Miller, Reliability and Validity in Qualitative Research; Mark Litwin, 

“How to measure survey reliability and validity”; William Trochim, The Research Methods 

Knowledge Base. 

24

FINDINGS 

We get all kinds of sourced material, but I think I trust technical collection 

more than the other INTs. 

I try to use everything we get, but, if we are jammed, I rely on 

sources we collect. 

Our value-added is classified sourcing. Everybody has access to the 

Web and CNN. 

All our customers are analysts these days. What we bring to the 

party is information no one else has. 

We’re in the business of secrets. If you see that stamped on something, 

it must be there for a reason. 

The over reliance on classified information for hypothesis testing creates a 

situation in which the data are screened and sorted by the organization before 

they are selected and tested by the analysts. Classified information comes 

from very specific types of technical and human sources, and it is filtered 

through very specific reporting channels. It also has a tendency to become 

homogeneous because of the source types and reporting mechanisms. Because 

it is generated and packaged in specific formats using specific processes, classified 

information lacks the diversity that is inherent in open information, and 

this contributes to confirmation bias. 

In sum, operating under difficult time constraints, trying to make new work 

accord with previous products, trying to maintain the prestige and power of 

the organization, and assigning greater weight to secret information than to 

open information have a cumulative effect, and the analyst often finds himself 

or herself trying to produce daily products using the most time-efficient strategies 

available instead of generating or testing hypotheses by way of refutation. 

The persistence of the notion of tradecraft, coupled with organizational 

norms, promotes the use of disjointed analytic strategies by separating intelligence 

analysts from other scientific disciplines. These conditions have had an 

effect on the self-concept of analysts and have molded the way analysts perceive 

their own identity. 

Finding: Analytic Identity 

The self is something which has a development; it is not initially 

there, at birth, but arises in the process of social experience and 

activity, that is, develops in the given individual as a result of his 

relations to that process as a whole and to other individuals within 

that process. 

George Mead. 17 

25

CHAPTER TWO 

Asked to define their profession, the majority of analysts described the process 

of analysis rather than the actual profession. The question, “What is an 

intelligence analyst?” resulted most often in a description of the work day and 

of the production cycle of analytic products and very seldom in an explanation 

of analytic methodology or a definition of an analyst outside of some specific 

context. With very few exceptions, analysts did not describe intelligence analysis 

as its own discipline with its own identity, epistemology, and research tradition. 

This is not necessarily uncommon. When physicians are asked to describe 

their profession, they tend to respond with a specific subdiscipline: “I’m a cardio-thoracic 

surgeon,” for example. When asked for a more general description, 

however, they tend to respond, “I’m a doctor” or “I’m a physician.” That 

is, in selective, insular professional cultures, practitioners are able to define 

their role in both a specific and general fashion. Intelligence analysts had difficulty 

defining their professional identity in a general way and often relied on 

specific context to explain what it is that they do and, by extension, who they 

are. 

The perception of individual analysts regarding their professional identity 

was associated most often with their organization’s function or with their own 

educational background and not with intelligence analysis as its own unique 

discipline. 

I work counternarcotics. 

I work counterterrorism. 

I’m a military analyst. 

I’m a leadership analyst. 

I’m an economist. 

I’m a political scientist. 

In addition to these categories, many analysts described their professional 

identity in terms of intelligence collection methods or categories. 

I do all-source analysis. 

I’m a SIGINT analyst. 

I’m an IMINT analyst. 

I’m a technical analyst. 

17 

George Mead was an American pragmatist philosopher and social psychologist, who, with John 

Dewey, made the University of Chicago the home of pragmatist philosophy and the “Chicago 

School” of sociology at the end of the 19th century. George Mead, Mind, Self, and Society. 

26

FINDINGS 

The shift in focus to daily analytic products, the changes in the production 

cycle, and a heterogeneously defined professional discipline have had an additional 

effect on the professional identity of analysts within the Intelligence 

Community. Analysts often commented that they perceived their job and their 

daily work routine as more akin to reporting than to analysis. 

Basically, on a day-to-day basis, it’s like working at CNN, only 

we’re CNN with secrets. Actually, it’s more like CNN’s Headline 

News. 

Imagine USA Today with spies—bullet points, short paragraphs, 

the occasional picture. You know, short and simple. 

I think of myself as a writer for the most important newspaper in the 

world. 

Many analysts expressed dissatisfaction with the shift in work processes 

from long-term forecasts and toward current reporting and the subsequent 

shift in their own professional identity within the Intelligence Community. 

The current sentiment about identity was often contrasted against an idealized 

past that was described as being freer of current production practices and 

products. 

About 15 years ago, I would have described myself as a scholar. 

Now, I’m a reporter. I’ve got 15 people trying to change my work 

into bullet points. Presumably, nobody has time to read anymore. 

When I joined, it seemed that the word “analyst” was shorthand for 

“problem solver.” Now, it’s shorthand for “reporter.” 

I’m proud of where I work. I’m proud of the job that we do. But, it is 

hard to take pride in one paragraph. I have to look at the big picture, 

or I would get discouraged. 

I spend most of my waking hours doing this, but I still can’t really 

say what an analyst is. 

I’m not a reporter, and I’m not an academic. I’m somewhere in 

between. 

The heterogeneous descriptions and definitions of intelligence analysis as a 

professional discipline were consistent findings during this study, indicating 

that there needs to be a clear articulation and dissemination of the identity and 

epistemology of intelligence analysis. A clearly defined professional identity 

would help to promote group cohesion, establish interagency ties and relationships, 

and reduce intra- and interagency communication barriers by establishing 

a professional class throughout the Intelligence Community. At an 

individual level, a clearly defined professional identity helps to reduce job dis- 

27

CHAPTER TWO 

satisfaction and anxiety by giving larger meaning to an individual’s daily 

actions. 18 

Finding: Analytic Training 

When I started, there wasn’t much training available. There were a 

few advanced courses, but, for the most part, it was on the job. 

A professional identity is generally a disciplinary norm, and it regularly 

occurs in other domains that are as cognitively demanding as intelligence 

analysis, such as medicine, aeronautics, and jurisprudence. These other 

domains practice a general system of professional enculturation that 

progresses from a basic education program to specialized training. 19 These 

training programs help to differentiate communities of practitioners from the 

general public, create specific and unique professional identities, and develop 

basic communication and task-specific skills. They also help the profession to 

continue to advance through formal research efforts. 

This is not the case within the Intelligence Community as a whole. Generally, 

the intelligence agencies that do provide basic and advanced training do 

so independently of other intelligence organizations. 20 A number of intelligence 

agencies do not provide basic analytic training at all or have only 

recently begun to do so, relying instead on on-the-job experiences and informal 

mentoring. 

We haven’t had a culture of training analysts here in the past. It’s 

only in the last year or so that we’ve started to change that. 

When I started here, analysts were considered administrative personnel. 

We didn’t have a training program. I think they just started 

one this year. 

My background was technical analysis, and we had a lot of operational 

training where I used to work. But now that I’m doing more 

strategic analysis, I’ve had to make it up as I go along. 

18 

Philip Cushman, Constructing the Self, Constructing America; Anthony Giddens, Modernity 

and Self-Identity; John P. Hewitt, Self and Society; Lewis P. Hinchman and Sandra K. Hinchman, 

Memory, Identity, Community; Carl Jung, The Undiscovered Self; George Levine, ed., Constructions 

of the Self. 

19 

Enculturation is the process or mechanism by which a culture is instilled in a human being from 

birth until death. In this instance, professional enculturation refers to the acquisition of a professional 

identity through specific cultural rituals and practices, as displayed, for example, by practitioners 

who have graduated from medical school, law school, and basic military training. 

20 

See footnote 7 in the Introduction for several recent cross-agency training initiatives. 

28

FINDINGS 

We have a basic training program, but it is different from the other 

agencies. Our mission is different. The problem is that we talk past 

each other all the time. 

When I got hired, I had an advanced degree. People assumed that, if 

I had a Masters, I could just figure out what I was supposed to do. 

The focus of training within the community varies widely and is shaped by 

the mission of the agency, such as technical, tactical, and operational. Many 

spend a considerable amount of time teaching new analysts how to prepare 

briefings, write papers, and perform administrative functions unique to their 

agency. This is logical from the perspective of agency managers, who naturally 

believe that investments made in personnel, training, and readiness ought 

to be tailored specifically for their own organizations. 

The problem with an agency-centric view is that, without a general communitywide 

training program for intelligence analysts, agencies and their analysts 

have difficulty finding, communicating, and interacting with one 

another. 21 Analysts often said they were disinclined to draw on resources outside 

of their own agency, indicating that either they do not know whom to 

contact or their experience in the past has been influenced by a strict organizational 

focus. 

The media keep talking about intelligence failures and communication 

breakdowns in the Intelligence Community. What do they 

expect? We don’t even speak the same language. 

It’s taken me 15 years to build my own network. If I didn’t have my 

own contacts, I wouldn’t know who to call. 

I don’t bother going outside. Our focus is different here. 

We have official channels, but it only really works if you trust the 

person on the other end of the phone. That’s hard to do if you don’t 

know them. 

Without an inclusive communitywide basic training program, differentiation 

between the intelligence analysis discipline, as a whole, and other fields 

of study is unlikely. A community of practitioners will have difficulty interacting 

with one another, communicating between and within organizations, and 

establishing a professional identity, which is a key ingredient in the development 

of a professional discipline. 

21 

Stephen Marrin, CIA’s Kent School: A Step in the Right Direction and “Improving CIA Analysis 

by Overcoming Institutional Obstacles.” 

29

PART II 

Ethnography of Analysis 

31

Taxonomy of Intelligence Analysis Variables 

Systemic Variables Systematic Variables Idiosyncratic Variables Communicative Variables 

Organization User Requirements Weltanschauung Formal 

Internal Operations (worldview) Inter-organization 

Structure Information Acquisition Affiliation Hierarchical 

Leadership Collection Methods Familial Inter-division 

Culture Overt Cultural Inter-group 

History Covert Ethnic Intra-organization 

Traditions Information Reliability Religious Hierarchical 

Social Practice 

Reproducible 

Social 

Intra-division 

Taboo 

Linguistic 

Intra-group 

Consistent 

Group Characteristics 

Political 

Individual 

Information Validity 

Psychology 

Hierarchical 

Hierarchy 

Historical 

Bias 

Inter-division 

Resources & Incentives Single Source Personality Profile Intra-group 

Manpower Dual Source Security Trust Informal 

Budget Triangulation Cognitive Processing Inter-organization 

Technology Information Archive Learning Style Hierarchical 

Assets 

Storage 

Information Acquisition 

Inter-group 


R&D 

Access 

Facilities Correlation Information Process- Intra-organization 

Work Groups-Teams 

External 

Consumer Needs 

Time and Imperatives 

Consumer Use 

Consumer Structure 

Consumer Hierarchy 

Conumer Reporting 

Retrieval 

Analytical Methodology 

Approach 

Intuitive 

Structured 

Semi-structured 

Information Processing 

Historical Information 

ing 

Expertise 

Problem-solving 

Decisionmaking 

Cognitive Load 

Speed/Accuracy 

Stress Effects 

Hierarchical 


Intra-group 

Individual 

Hierarchical 

Inter-group 

Intra-group 

Technology 

Politics Current Information Education Networked Analysis 

Internal-Organization Decision Strategies 

Domain 

Collaboration 

Policy 

Estimative 

Location 

Tradition Predictive Mentor 

Taboo 

Reporting 

Training 

Security/Access 

Verbal Methods 

Organizational 

External-National Written Methods Domain 

Law 

Procedural 

Policy 

Readiness 

External-International 

Resources 

Security 

Facilities 

Denial 

Deception 

Policy 

32

CHAPTER THREE 

A Taxonomy of Intelligence Variables 1 

Science is organized knowledge. 

Herbert Spencer 2 

Aristotle may be the father of scientific classification, but it was biologist 

Carolus Linnaeus who introduced the first formal taxonomy—kingdom, class, 

order, genera, and species—in his Systema Naturae in 1735. By codifying the 

naming conventions in biology, Linnaeus’s work provided a reference point 

for future discoveries. Darwin’s development of an evolutionary theory, for 

example, benefited greatly from Linnaeus’s creation of a hierarchical grouping 

of related organisms. The Systema Naturae taxonomy was not a fixed 

product but rather a living document. Linnaeus himself revised it through 10 

editions, and later biologists have continued to modify it. 3 

In response to new discoveries and the development of new research methods 

in other domains, taxonomies were created to help organize those disciplines 

and to assist researchers in identifying variables that required additional 

study. The development of specific taxonomies—from highly structured systems, 

such as the periodic table of chemical elements, to less structured 

approaches, such as Bloom’s Taxonomy 4 —is a key step in organizing knowl- 

1 

A version of this chapter, “Developing a Taxonomy of Intelligence Analysis Variables,” originally 

appeared in Studies in Intelligence 47, no. 3 (2003): 61–71. 

2 

Herbert Spencer’s The Study of Sociology, published in 1874, set the stage for the emergence of 

sociology as a discipline. 

3 

Ernst Haeckel introduced phylum to include related classes and family to include related genera in 

1866. The Linnaeus taxonomy is currently being revised to accommodate genomic mapping data. 

33

CHAPTER THREE 

edge and furthering the growth of individual disciplines. A taxonomy differentiates 

domains by specifying the scope of inquiry, codifying naming 

conventions, identifying areas of interest, helping to set research priorities, 

and often leading to new theories. Taxonomies are signposts, indicating what 

is known and what has yet to be discovered. 

This chapter, to which more than 100 individuals contributed their time and 

advice, proposes a taxonomy for the field of intelligence. It is my hope that the 

resulting organized listing of variables will help practitioners strengthen their 

understanding of the analytic process and point them in directions that need 

additional attention. 

Intelligence Analysis 

We could have talked about the science of intelligence, but . . . the 

science of intelligence is yet to be invented. 

Charles Allen 5 

Developing an intelligence taxonomy is complicated by the fact that the literature 

in the field is episodic and reflects specialized areas of concern. Perhaps 

it is best to begin with what appears to be a key distinction between 

general analysis and intelligence analysis, that of solving a problem in the 

public domain, and solving a problem in a private or secret domain. 

Ronald Garst articulates two arguments that are used to support this distinction: 

intelligence analysis is more time sensitive than analysis in other 

domains and it deals with information that intentionally may be deceptive. 6 

The notion that intelligence is uniquely time sensitive is questionable, however. 

Intelligence is not the only domain where time constraints can force 

decisions to be made before data are complete. Time is always a key variable, 

whether one is in an operating room or in a cockpit. To be sure, intelligence is 

a life and death profession, but so are medicine and mass transportation. In 

each instance, failure can mean casualties. 

Garst’s point about intentional deception is more germane. With the possible 

exception of business and financial markets, analysts in other fields seldom 

deal with intentional deception. As discussed in Chapter One, Michael 

Warner makes a good case for secrecy being the primary variable distinguish- 

4 

See Benjamin S. Bloom, Taxonomy of Educational Objectives. Bloom’s taxonomy is a classification 

of levels of intellectual behavior in learning, including knowledge, comprehension, application, 

analysis, synthesis, and evaluation. 

5 

Comment made by the Associate Director of Central Intelligence for Collection at a public seminar 

on intelligence at Harvard University, spring 2000. 

6 

Ronald Garst, A Handbook of Intelligence Analysis. 

34

A TAXONOMY OF INTELLIGENCE 

ing intelligence from other such activities. 7 He argues that the behavior of the 

subject of intelligence changes if the subject is aware of being observed or 

analyzed. As discussed earlier, Warner’s argument is supported by a long history 

of psychological research, beginning with an experimental program 

between 1927 and 1930 at Western Electric’s Hawthorne Works in Chicago. 8 

Intentional deception can occur outside intelligence—in connection with 

certain law enforcement functions, for example—but most of the professional 

literature treats this as the exception rather than the rule. In the case of intelligence 

analysis, deception is the rule; the validity of the data is always in 

doubt. Moreover, intelligence analysts are specifically trained to take deception 

into account as part of the analytic process—to look for anomalies and 

outliers instead of focusing on the central tendencies of distribution. 

The taxonomy being developed here requires a definition of intelligence 

analysis that is specific to the field. Intelligence pioneer Sherman Kent, who 

saw intelligence as a “special category of knowledge,” laid the foundation for 

understanding the activities inherent in intelligence analysis by demonstrating 

that the analytic process itself was subject to being analyzed. 9 Kent’s approach 

to analysis was to reduce the process to smaller functional components for 

individual study. 10 For example, he described intelligence analysis as having a 

basic descriptive element, a current reporting element, and an estimative element. 

Following suit, other authors focused attention on the process or methodological 

elements of intelligence analysis. In Intelligence Research Methodology, 

Jerome Clauser and Sandra Weir followed Kent’s three functional areas 

and went on to describe basic research foundations and the inductive and 

deductive models for performing intelligence analysis. 11 Garst’s Handbook of 

Intelligence Analysis contains less background in basic research methods than 

Clauser and Weir’s book, but it is more focused on the intelligence cycle. 12 

Bruce Berkowitz and Allan Goodman highlight the process of strategic 

intelligence and define intelligence analysis as: “[T]he process of evaluating 

and transforming raw data into descriptions, explanations, and conclusions for 

intelligence consumers.” 13 Lisa Krizan, too, focuses on process. She writes 

7 

Michael Warner. 

8 

The Hawthorne Effect. See footnote 5 in Chapter Two. 

9 

Sherman Kent, Strategic Intelligence for American World Policy. 

10 

See Chapter Seven for a fuller discussion of this approach, now usually referred to as metaanalysis. 

11 

Jerome K. Clauser and Sandra M. Weir, Intelligence Research Methodology. 

12 

See also: Morgan Jones, The Thinker’s Toolkit. Jones’s book is a popular version of the work of 

Garst and Clauser and Weir in that it describes a collection of analytic methods and techniques for 

problem-solving; however, the methods are not necessarily specific to intelligence. 

35

CHAPTER THREE 

that, “At the very least, analysis should fully describe the phenomenon under 

study, accounting for as many relevant variables as possible. At the next 

higher level of analysis, a thorough explanation of the phenomenon is 

obtained, through interpretation of the significance and effects of its elements 

on the whole.” 14 In addition, several authors have written about individual analytic 

approaches. 15 

Although the referenced works focus on methods and techniques, they do 

not suggest that analysis is limited to these devices. The view that analysis is 

both a process and a collection of specific techniques is explicit in the above 

definitions. Analysis is seen as an action that incorporates a variety of tools to 

solve a problem. Different analytic methods have something to offer different 

analytic tasks. 

Although largely implicit in the above definitions, analysis is also seen as a 

product of cognition, and some authors directly link the two. Robert Mathams 

defines analysis as: “[T]he breaking down of a large problem into a number of 

smaller problems and performing mental operations on the data in order to 

arrive at a conclusion or generalization.” 16 Avi Shlaim writes: “Since the facts 

do not speak for themselves but need to be interpreted, it is inevitable that the 

individual human propensities of an intelligence officer will enter into the process 

of evaluation.” 17 Yet others describe analysis as a process whereby: 

“[I]nformation is compared and collated with other data, and conclusions that 

also incorporate the memory and judgment of the intelligence analyst are 

derived from it.” 18 

Several authors make the case that analysis is not just a product of cognition 

but is itself a cognitive process. J. R. Thompson and colleagues write that 

“[I]ntelligence analysis is an internal, concept-driven activity rather than an 

external data-driven activity.” 19 In his Psychology of Intelligence Analysis, 

Heuer observes: “Intelligence analysis is fundamentally a mental process, but 

understanding this process is hindered by the lack of conscious awareness of 

the workings of our own minds.” 20 Ephraim Kam comments: “The process of 

intelligence analysis and assessment is a very personal one. There is no 

agreed-upon analytical schema, and the analyst must primarily use his belief 

13 

Bruce D. Berkowitz and Allan E. Goodman, Strategic Intelligence for American National Security, 

85. See Chapter Four for more on the intelligence cycle. 

14 

Lisa Krizan, Intelligence Essentials for Everyone. 

15 

See the apprendix for a listing of the literature. 

16 

Robert Mathams, “The Intelligence Analyst’s Notebook.” 

17 

Avi Shlaim, “Failures in National Intelligence Estimates: The Case of the Yom Kippur War.” 

18 

John Quirk et al., The Central Intelligence Agency: A Photographic History. 

19 

J. R. Thompson, R. Hopf-Weichel, and R. Geiselman, The Cognitive Bases of Intelligence 

Analysis. 

20 

Richards J. Heuer, Jr., Psychology of Intelligence Analysis. 

36


system to make assumptions and interpret information. His assumptions are 

usually implicit rather than explicit and may not be apparent even to him.” 21 

These definitions reflect the other end of the spectrum from those concerned 

with tools and techniques. They suggest that the analytic process is a 

construction of the human mind and is significantly different from individual 

to individual or group to group. Certainly, Kam goes farthest along this path, 

but even he does not suggest that one forgo tools; rather, he says that the process 

of choosing the tool is governed by cognition as well. 

Recognizing that the scope of intelligence analysis is so broad that it 

includes not only methods but also the cognitive process is a significant step. 

Viewing analysis as a cognitive process opens the door to a complex array of 

variables. The psychology of the individual analyst must be considered, along 

with individual analytic tools. In the broadest sense, this means not merely 

understanding the individual psyche but also understanding the variables that 

interact with that psyche. In other words, intelligence analysis is the sociocognitive 

process, 22 occurring within a secret domain, by which a collection of 

methods is used to reduce a complex issue to a set of simpler issues. 

Developing the Taxonomy 

The first step of science is to know one thing from another. This 

knowledge consists in their specific distinctions; but in order that it 

may be fixed and permanent distinct names must be given to different 

things, and those names must be recorded and remembered. 

Carolus Linnaeus 

My research was designed to isolate variables that affect the analytic process. 

The resulting taxonomy is meant to establish parameters and to stimulate 

dialogue in order to develop refinements. Although a hierarchic list is artificial 

and rigid, it is a first step in clarifying areas for future research. The actual 

variables are considerably more fluid and interconnected than such a structure 

suggests. Once the individual elements are refined through challenges in the 

literature, they might be better represented by a link or web diagram. 23 

To create this intelligence analysis taxonomy, I used Alexander Ervin’s 

applied anthropological approach, which employs multiple data collection 

methods to triangulate results. 24 I also drew on Robert White’s mental work- 

21 

Ephraim Kam, Surprise Attack. The Victim’s Perspective, 120 

22 

That is, analysis does not occur in a vacuum. It is socially constructed. See Lev Vygotsky, Mind 

and Society. 

23 

See Chapter Four for Judith Meister Johnston’s systems analysis approach to describing the fluidity 

of the intelligence process. 

37

CHAPTER THREE 

load model, David Meister’s behavioral model, and the cognitive process 

model of Gary Klein and his colleagues. 25 Each model focuses on a different 

aspect of human performance: White’s examines the actual task and task 

requirements; Meister’s looks at the behavior of individuals performing a 

task; and Klein’s uses verbal protocols to identify the cognitive processes of 

individuals performing a task. 

Surveying the literature. My research began with a review of the literature, 

both for background information and for the identification of variables. The 

intelligence literature produced by academics and practitioners tends to be 

episodic, or case-based. This is not unique to the field of intelligence. A number 

of disciplines—medicine, business, and law, for example—are also casebased. 

Many of the texts were general or theoretical rather than episodic. 

Again, this is not an uncommon phenomenon. The review yielded 2,432 case 

studies, journal articles, technical reports, transcripts of public speeches, and 

books related to the topic. I then narrowed the list to 374 pertinent texts on 

which a taxonomy of intelligence analysis could be built, and I analyzed them 

to identify individual variables and categories of variables that affect intelligence 

analysis. 26 

Using a methodology known as “Q-Sort,” by which variables are sorted and 

categorized according to type, I read each text and recorded the variables that 

each author identified. 27 These variables were then sorted by similarity into 

groups. Four broad categories of analytic variables emerged from this process. 

28 

Refining the prototype. Next, I used the preliminary taxonomy derived from 

my reading of the literature to structure interviews with 51 substantive experts 

and 39 intelligence novices. In tandem, I conducted two focus group sessions, 

with five individuals in each group. As a result of the interviews and focus 

group discussions, I added some variables to each category, moved some to 

different categories, and removed some that appeared redundant. 

Testing in a controlled setting. Finally, to compare the taxonomy with specific 

analytic behaviors, I watched participants in a controlled intelligence 

analysis–training environment. Trainees were given information on specific 

24 

Alexander Ervin, Applied Anthropology. See Chapter One, note 4 for a definition of triangulation. 

25 

Robert White, Task Analysis Methods; David Meister, Behavioral Analysis and Measurement 

Methods; G. Klein, R. Calderwood, and A. Clinton-Cirocco, Rapid Decision Making on the Fire 

Ground. 

26 

A copy of the list and search criteria is available from the author. 

27 

William Stephenson, The Study of Behavior: Q-Technique and its Methodology. See Chapter 

Eleven for additional information on this methodology. 

28 

I would like to credit Dr. Forrest Frank of the Institute for Defense Analyses for his suggestions 

regarding the naming convention for the categories of variables in the accompanying chart. 

38


cases and directed to use various methods to analyze the situations and to generate 

final products. During the training exercises, the verbal and physical 

behavior of individuals and groups were observed and compared with the taxonomic 

model. I participated in a number of the exercises myself to gain a better 

perspective. This process corroborated most of the recommendations that 

had been made by the experts and novices and also yielded additional variables 

for two of the categories. 

The resulting taxonomy is purely descriptive. It Systemic Variables 

is not intended to demonstrate the weight or Organization 

Internal 

importance of each variable or category. That is, 

Structure 

the listing is not sufficient to predict the effect of 

any one variable on human performance. The 

intention of the enumeration is to provide a framework 

for aggregating existing data and to create a 

foundation for future experimentation. Once the 

variables have been identified and previous findings 

have been aggregated, it is reasonable to consider 

experimental methods that would isolate and 

control individual variables and, in time, indicate 

sources of error and potential remediation 

Systemic Variables 

The column of Systemic Variables incorporates 

items that affect both an intelligence organization 

and the analytic environment. Organizational 

variables encompass the structure of the intelligence 

organization; leadership, management, and 

management practices; history and traditions; the 

working culture, social practices within the organization, 

and work taboos; and organizational 

demographics. They also include internal politics, 

the hierarchical reporting structure, and material 

and human resources. Industrial and organizational 

psychology, sociology, and management 

studies in business have brought attention to the 

importance of organizational behavior and its 

effect on individual work habits and practices. 

The works of Allison, Berkowitz and Goodman, 

Elkins, Ford, Godson, and Richelson, among others, 

examine in general the organizational aspects 

Leadership 

Culture 

History 

Traditions 

Social Practice 

Taboo 

Group Characteristics 

Hierarchy 

Resources and Incentives 

Manpower 

Budget 

Technology 

Assets 

R&D 

Facilities 

Work Groups-Teams 

External 

Consumer Needs 

Time and Imperatives 

Consumer Use 

Consumer Structure 

Consumer Hierarchy 

Conumer Reporting 

Politics 

Internal-Organization 

Policy 

Tradition 

Taboo 

Security/Access 

External-National 

Law 

Policy 

External-International 

Security 

Denial 

Deception 

Policy 

of intelligence. 29 39

CHAPTER THREE 

The Systemic Variables category also focuses on 

environmental variables. These include such external 

influences on the organization as consumer 

needs and requirements, time limitations, and methods 

for using the information; and the consumer’s 

Overt 

Covert 

organization, political constraints, and security 

issues. The works of Betts, Hulnick, Hunt, Kam, 

Reproducible and Laqueur address the environmental and con- 

Consistent sumer issues that affect intelligence analysis. 30 

Systematic Variables 

User Requirements 

Operations 


Collection Methods 

Information Reliability 

Information Validity 

Historical 

Single Source 

Dual Source 

Triangulation 

Information Archive 

Storage 

Access 

Correlation 

Retrieval 

Analytical Methodology 

Approach 

Intuitive 

Structured 

Semi-structured 


Historical Information 

Current Information 

Decision Strategies 

Estimative 

Predictive 

Reporting 

Verbal Methods 

Written Methods 

Case studies that touch on various systemic variables 

include: Allison, on the Cuban missile crisis; 

Betts, on surprise attacks; Kirkpatrick, on World 

War II tactical intelligence operations; Shiels, on 

government failures; Wirtz, on the Tet offensive in 

Vietnam; and Wohlstetter, on Pearl Harbor. 31 

Systematic Variables 

The Systematic Variables are those that affect 

the process of analysis itself. They include the 

user’s specific requirements, how the information 

was acquired, the information’s reliability and 

validity, how the information is stored, the prescribed 

methods for analyzing and processing the 

information, specific strategies for making decisions 

about the information, and the methods used 

to report the information to consumers. 

A number of authors have written about the 

analytic tools and techniques used in intelligence, 

among them Clauser and Weir, on intelligence research methods; Jones, on 

analytic techniques; and Heuer, on alternative competing hypotheses. 

29 

Graham T. Allison, Essence of Decision; Bruce D. Berkowitz and Allan E. Goodman, Best Truth; 

Dan Elkins, An Intelligence Resource Manager’s Guide; Harold Ford, Estimative Intelligence; Roy 

Godson, Comparing Foreign Intelligence; Jeffrey Richelson, The U.S. Intelligence Community. 

30 

Richard K. Betts, “Policy-makers and Intelligence Analysts: Love, Hate or Indifference”; Arthur 

S. Hulnick, “The Intelligence Producer-Policy Consumer Linkage: A Theoretical Approach”; David 

Hunt, Complexity and Planning in the 21st Century; Kam, Surprise Attack; Walter A. Laqueur, The 

Uses and Limits of Intelligence. 

31 

Allison; Richard K. Betts, Surprise Attack; Lyman B. Kirkpatrick, Jr., Captains Without Eyes: 

Intelligence Failures in World War II; Frederick L. Shiels, Preventable Disasters: Why Governments 

Fail; James J. Wirtz, The Tet Offensive: Intelligence Failure in War; Roberta Wohlstetter, Pearl Harbor: 

Warning and Decision. 

40


Comparatively little work has been done 

comparing structured techniques to intuition. 

Robert Folker’s work is one of the exceptions; it 

compares the effectiveness of a modified form of 

alternative competing hypotheses with intuition 

in a controlled experimental design. His study is 

unique in the field and demonstrates that 

experimental methods are possible. Geraldine 

Krotow’s research, on the other hand, looks at 

differing forms of cognitive feedback during the 

analytic process and makes recommendations to 

improve intelligence decisionmaking. 32 

Idiosyncratic Variables 

Variables in the third column are those that 

influence individuals and their analytic performance. 

These include the sum of life experiences 

and enculturation—familial, cultural, ethnic, religious, 

linguistic, and political affiliations—that 

identify an individual as a member of a group. I 

have used the German word Weltanschauung 

(customarily rendered in English as “world view”) 

to denote this concept. These idiosyncratic variables 

also encompass such psychological factors 

as biases, personality profiles, cognitive styles and 

processing, cognitive loads, 33 expertise, approach 

to problem-solving, decisionmaking style, and 

reaction to stress. Finally, there are such domain 

variables as education, training, and the readiness 

to apply knowledge, skills, and abilities to the task 

at hand. 

Idiosyncratic Variables 

Weltanschauung (worldview) 

Affiliation 

Familial 

Cultural 

Ethnic 

Religious 

Social 

Linguistic 

Political 

Psychology 

Bias 

Personality Profile 

Security Trust 

Cognitive Processing 

Learning Style 



Expertise 

Problem-solving 

Decisionmaking 

Cognitive Load 

Speed/Accuracy 

Stress Effects 

Education 


Location 

Mentor 




Procedural 

Readiness 

Resources 

Facilities 

The relevant psychological literature is extensive. Amos Tversky and Daniel 

Kahneman began to examine psychological biases in the early 1970s. 34 Their 

work has found its way into the intelligence literature through Butterfield, 

32 

Geraldine Krotow, The Impact of Cognitive Feedback on the Performance of Intelligence Analysts, 

176. 

33 

“Cognitive loads” are the amount/number of cognitive tasks weighed against available cognitive 

processing power. 

34 

Amos Tversky and Daniel Kahneman, “The Belief in the ‘Law of Small Numbers’” and “Judgment 

Under Uncertainty: Heuristics and Biases.” 

41

CHAPTER THREE 

Davis, Goldgeier, and Heuer, among others. 35 Decisionmaking and problemsolving 

have been studied since the early 1920s, and these topics are reflected in 

Heuer’s work as well. 36 Personality-profiling, too, 

Communicative Variables is well understood and has had an impact on recent 

Formal intelligence practices and theory. 37 

Inter-organization 

Hierarchical 


Inter-group 

Intra-organization 

Hierarchical 


Intra-group 

Individual 

Hierarchical 


Intra-group 

Informal 

Inter-organization 

Hierarchical 


Inter-group 

Intra-organization 

Hierarchical 


Intra-group 

Individual 

Hierarchical 

Inter-group 

Intra-group 

Technology 

Networked Analysis 

Collaboration 

Other well-researched areas, however, have yet 

to be studied in the context of intelligence. Acculturation 

and enculturation, educational factors, 

and training strategies, for example, may yet yield 

interesting results and insights into the field of 

intelligence. 38 

Communicative Variables 

The fourth category contains variables that 

affect interaction within and among groups. 

Because communication is the vital link within 

the system—among processes and among individuals—this 

group of variables logically could be 

included in each of the other three categories. Its 

broad relevance, however, makes it seem reasonable 

to isolate it as a distinct area of variability. 

The Communicative Variables include formal and 

informal communications within an organization 

(from products to e-mails), among organizations, 

and between individuals and the social networks 

they create. In his essay on estimative probability, 

Kent highlights this area by describing the difficulty 

that producers of intelligence have in communicating 

the likelihood of an event to their consumers. 39 In addition to 

addressing organizational issues, case studies by Wohlstetter and others touch 

on communication and social networks and the impact that communication 

35 

Alexander Butterfield, The Accuracy of Intelligence Assessment; Jack Davis, “Combating 

Mindset”; James M. Goldgeier, “Psychology and Security”; Heuer. 

36 

Frank H. Knight, Risk, Uncertainty and Profit. 

37 

Caroline Ziemke, Philippe Loustaunau, and Amy Alrich, Strategic Personality and the Effectiveness 

of Nuclear Deterrence. 

38 

Acculturation is the cultural change that occurs in response to extended firsthand contact 

between two or more previously autonomous groups. It can result in cultural changes in groups as 

well as individuals. 

39 

Sherman Kent, “Words of Estimative Probability.” 

42


has on the analytic process. 40 This is an area that could benefit from additional 

study. 

Conclusion 

There is rarely any doubt that the unconscious reasons for practicing 

a custom or sharing a belief are remote from the reasons given 

to justify them. 

—Claude Levi-Strauss 41 

As it is now practiced, intelligence analysis is art, tradecraft, and science. 

There are specific tools and techniques to help perform the tasks, but, in the 

end, it is left to individuals to use their best judgment in making decisions. 

This is not to say that science is not a part of intelligence analysis. Science is 

born of organized knowledge, and organizing knowledge requires effort and 

time. The work on this taxonomy is intended to help that process by sparking 

discussion, identifying areas where research exists and ought to be incorporated 

into the organizational knowledge of intelligence, and identifying areas 

where not enough research has been performed. 

There are a number of parallels in the field of medicine, which, like intelligence, 

is art, tradecraft, and science. To solve problems, practitioners are 

trusted to use their best judgment by drawing on their expertise. What is 

important to remember is that there are numerous basic sciences driving medical 

practice. Biology, chemistry, physics, and all of the subspecialties blend 

together to create the medical sciences, the foundation on which modern medicine 

rests. The practice of medicine has been revolutionized by the sciences 

that underpin its workings. 

Intelligence analysis has not experienced that revolution. Unlike medicine, 

the basic sciences that underpin intelligence are the human sciences, which are 

considerably more multivariate and more difficult to control. Because of these 

factors, it is a more complex task to measure “progress” in the human sciences. 

Even so, there are numerous domains from which intelligence may borrow. 

Organizational behavior is better understood today than ever before. 

Problem-solving and decisionmaking have been researched since the 1920s. 

40 

Wohlstetter. 

41 

Claude Levi-Strauss wrote Structural Anthropology in 1958, setting the stage for structuralism 

to emerge as an analytic interpretive method. Broadly, structuralism seeks to explore the interrelationships 

(the “structures”) through which meaning is produced within a culture. This meaning, 

according to structural theory, is produced and reproduced through various practices, phenomena, 

and activities that serve as systems of “signification.” A structuralist studies activities as 

diverse as food preparation and serving rituals, religious rites, games, literary and non-literary 

texts, and forms of entertainment to discover the ways in which cultural significance develops. 

43

CHAPTER THREE 

Structural anthropology addresses many of the enculturation and identity issues 

that affect individual behavior. Cognitive scientists are building models that can 

be tested in experimental conditions and used for developing new tools and techniques. 

Sociology and social theory have much to offer in studying social networks 

and communication. 

The organization of knowledge in intelligence is not a small task, but I 

believe that the effort should be undertaken for the betterment of the profession. 

The taxonomy proposed here could serve as a springboard for a number 

of innovative projects, for example: development of a research matrix that 

identifies what is known and how that information may be of use in intelligence 

analysis, setting a research agenda in areas of intelligence that have 

been insufficiently studied, application of research from other domains to 

develop additional training and education programs for analysts, creation of a 

database of lessons learned and best practices to build a foundation for an 

electronic performance support system, integration of those findings into new 

analytic tools and techniques, and development of a networked architecture 

for collaborative problem-solving and forecasting. It is my hope that this taxonomy 

will help intelligence practitioners take steps in some of these new 

directions. 

44

CHAPTER FOUR 

Testing the Intelligence Cycle Through Systems 

Modeling and Simulation 

Judith Meister Johnston 1 

Rob Johnston 

Throughout the Intelligence Community, the process of analysis is represented 

conventionally by a model known as the Intelligence Cycle (See next page). 

Unfortunately, the model omits elements and fails to capture the process accurately, 

which makes understanding the challenges and responsibilities of intelligence 

analysis much more difficult. It also complicates the tasks of recognizing 

where errors can occur and determining methods for change based on accurate 

predictions of behavior. Our analysis of the Intelligence Cycle, employing a systems 

approach and a simulation created to represent it, demonstrated these shortcomings. 

2 Because of its wide acceptance and use in training and in discussions 

of the analytic process, the traditional representation of the Intelligence Cycle 

will be closely considered in this chapter, especially with regard to its impact on 

analytic products, its effectiveness, and its vulnerability to error and failure. 

The Traditional Intelligence Cycle 

The Intelligence Cycle is customarily illustrated as a repeating process consisting 

of five steps. 3 Planning and direction encompasses the management of 

1 

Dr. Judith Meister Johnston is an educational psychologist with expertise in human performance 

technology and instructional systems design. A Booz Allen Hamilton Associate, she supports 

human factors work for the Intelligence Community. 

2 

Simulation involves the development of a computer-based model that represents the internal 

processes of an event or situation and estimates the results of proposed actions. 

45

CHAPTER FOUR 

the entire effort and involves, in particular, determining collection requirements 

based on customer requests. Collection refers to the gathering of raw data to 

meet the collection requirements. These data can be derived from any number 

and type of open and secret sources. Processing refers to the conversion of raw 

data into a format analysts can use. Analysis and production describes the process 

of evaluating data for reliability, validity, and relevance; integrating and 

analyzing it; and converting the product of this effort into a meaningful whole, 

which includes assessments of events and implications of the information collected. 

Finally, the product is disseminated to its intended audience. 4 

In some ways, this 

The Traditional Intelligence Cycle process resembles many 

other production cycles. It 

is prescriptive, structured, 

Planning 

and direction 

made up of discrete steps, 

and expected to yield a 

specific product. The 

traditional depiction of the 

Collection 

Dissemination 

process in the Intelligence 

Cycle, however, is not an 

accurate representation of 

the way intelligence is 

produced. The notion of a 

Processing Analysis cycle assumes that the steps 

and production 

will proceed in the 

prescribed order and that 

the process will repeat itself continuously with reliable results. This type of 

representation gives the impression that all inputs are constant and flow 

automatically, but it does not address elements that may influence the 

movement of the cycle, positively or negatively. 

The most significant assumption about the Intelligence Cycle model, that it 

provides a means for helping managers and analysts deliver a reliable product, 

should be examined at the outset. This can be accomplished through two types 

of analyses. The first is a systematic examination of the elements of the process, 

the inputs it relies on, and the outcomes that can be expected. The second 

uses a systemic approach to identifying the relationships of the elements in the 

process and their influence on each other. 

3 

Central Intelligence Agency, A Consumer’s Guide to Intelligence. 

4 

Central Intelligence Agency, Factbook on Intelligence. 

46

TESTING THE INTELLIGENCE CYCLE 

Systematic Analysis 

Many disciplines (for example, business process, organizational management, 

human performance technology, program evaluation, systems engineering, 

and instructional systems design) employ specific methods to analyze the 

effectiveness of products, programs, or policy implementation. Although they 

are often given different, domain-specific names and may involve varying levels 

of detail, these analytic methods involve the identification of inputs, processes, 

and outputs. Once these elements are identified, the evaluation process 

maps the relationships of the inputs, their implementation in processes, and 

their impact on intended—as opposed to actual—outputs. 5 The reasoning 

underlying this approach is that an effective product, result, or action is one 

that matches its objectives and that these objectives are reached by processes 

that logically lead from the objectives to results. Along the way, existing practices 

and barriers to reaching goals effectively can be identified. Finally, interventions, 

which can range in complexity from simple job aids to a complete 

restructuring of the process, can be proposed and implemented and their 

impacts assessed. 6 

This method of analysis has been employed successfully to evaluate 

processes that have characteristics similar to the Intelligence Cycle, and 

we use it here to examine the effectiveness of the Intelligence Cycle and 

its utility in representing the creation of sound analytic products while 

avoiding failure or error. 

Findings Based on Systematic Analysis 

The Intelligence Cycle is represented visually to provide an easy-to-grasp and 

easy-to-remember representation of a complex process. Although this type of representation 

may make the flow of information and the interrelationships of steps 

easy to identify, it does not indicate who or what may affect the completion of a 

step or the resources needed to begin the next step. In its concise form, then, the 

visual representation of the Intelligence Cycle is reduced to a map of information 

handling. Without explicit descriptions of the steps in the process or the benefit of 

prior knowledge, it can raise questions of accuracy and completeness and can 

occasion misconceptions, particularly concerning the roles and responsibilities of 

intelligence analysts. 

5 

Marc J. Rosenberg, “Performance technology: Working the system.” 

6 

Roger Kaufman, “A Holistic Planning Model: A Systems Approach for Improving Organizational 

Effectiveness and Impact.” 

47

CHAPTER FOUR 

Inputs, Processes, and Outputs of the Intelligence Cycle 

Inputs Processes Outputs 

Policymaker and other Direction Data collection 

stakeholder questions, 

requirements 

requirements 

Data collection Planning Task assignment, 

requirements, assessment 

potential data sources, 

of available resources and 

focus of analysis 

capabilities 

Open-source data: foreign Collection Potentially relevant data 

broadcasts, newspapers, 

periodicals, books; 

Classified data: case 

officer, diplomatic, and 

attaché reports, 

electronics, satellite 

photos 

Potentially relevant data 

Usable data 

Processing: Reduction 

of data in a variety of 

formats to consistent 

pieces of usable data 

Analysis: Integration, 

evaluation, assessment 

of reliability, validity, 

and relevance of data 

Production: Peer 

review, supervisory 

review 

Usable Data 

Findings 

Analytic review 

Written briefs, studies, 

long range assessments, 

short range assessments, 

oral briefs, national 

intelligence estimates 

Written briefs, studies, Dissemination Appropriate product to 

long-range assessments, 

address customer’s need 

short-range assessments, 

oral briefs, national 

intelligence estimates 

The table above depicts a more detailed input, process, and output analysis 

and makes some relationships clearer—for example, the steps that include two 

actions (planning and direction, analysis and production) have been separated 

into distinct processes—but it sill leaves a number of questions unanswered. It 

is difficult to see from this analysis specifically who is responsible for providing 

inputs, carrying out the processes, and producing outputs; and what 

requirements are expected of the inputs and outputs. 

An important issue that this analysis only partly clarifies is the role of analysts. 

Nor does it demonstrate how great a burden the process places on them, 

48


an especially important point. Assuming that the actions identified in the 

“Processes” column are ultimately the responsibility of the intelligence analyst, 

the steps of the process move from a heavy reliance on information coming 

in from sources outside the analyst’s control to a heavy reliance on the 

analyst to produce and manage the final submission of the product. 

Another important defect in this analysis is that steps in the cycle do not 

accurately represent the differences in the cognitive complexity involved in 

preparing a long-range assessment or a national intelligence estimate and that 

required for a two-paragraph brief on a current situation. The same can be said 

about the process required to develop each of the products. 

The Intelligence 

Treverton’s “Real” Intelligence Cycle 

Cycle depicts a 

sequential process and 

does not provide for 

iterations between 

steps. This is not an 

accurate reflection of 

what happens, particularly 

in the collection 

and production steps, 

where the challenges 

of defining policymaker 

needs and shaping 

collection 

necessitate repeated 

refinement of requirements 

by policymakers 

or of inferences by the 

Intelligence Community. 

A more accurate picture of the steps in the process and their iterative tendencies 

may be seen in Greg Treverton’s model, which he terms the “Real” 

Intelligence Cycle (above). 7 

Mark Lowenthal proposes another model. 8 Although presented in a more 

linear fashion than Treverton’s, it focuses on the areas where revisions and 

reconsiderations take place, representing iteration in a slightly different light. 

Both models provide a more realistic view of the entire process. In addition, 

assuming that the analyst’s role is represented by the “Processing, Analysis” 

box, the Treverton model allows us to focus visually and conceptually on the 

demands that the process can place on the analyst. However, neither model 

7 

Gregory F. Treverton, Reshaping National Intelligence in an Age of Information. 

8 

Mark W. Lowenthal, Intelligence: From Secrets to Policy. 

49

CHAPTER FOUR 

provides an effective way of showing who is responsible for what, and neither 

reflects the impact of the work on the individuals responsible for producing 

the reports—particularly the analyst—nor the reliance of the analyst on a variety 

of factors beyond his or her control. 

In sum, this brief evaluation of the Intelligence Cycle with respect to its 

inputs, processes, and outputs shows us that the traditional model: 

• assumes the process works the same way for all objectives, regardless of 

complexity and cognitive demands; 

• does not represent the iterative nature of the process required for meeting 

objectives; 

• does not identify responsibilities for completing steps and allows for misconceptions 

in this regard; 

• does not accurately represent the impact of resource availability on analysts. 

To better understand these limitations and the relationships among elements 

in the process, it is necessary to step back and take a longer view of the process, 

using a different method of analysis. 

Systemic Analysis 

If we think of the phenomenon that is being described by the Intelligence 

Cycle as a system and perform a systems analysis, we may be able to derive a 

greater understanding of process relationships, a better representation of the 

variables affecting the process, and a greater level of detail regarding the process 

itself. 

The premise that underlies systems analysis as a basis for understanding 

phenomena is that the whole is greater than the sum of its parts. A systems 

analysis allows for the inclusion of a variety of influences and for the identification 

of outliers that are obfuscated in other types of analyses but that often 

play major roles. A systems analysis is accomplished through the examination 

of phenomena as cause-and-effect patterns of behavior. This approach is 

called a “closed feedback loop” in systems analysis. It requires a close examination 

of relationships and their influences, provides a longer view of these 

relationships, and often reveals new insights based on trends rather than on 

discrete events. 9 

The systems model diagrammed below is a visual representation of the process. 

The elements of the Intelligence Cycle are identified in terms of their 

9 

Fritjof Capra, “Criteria of Systems Thinking”; David L. Kaufman, Jr., Introduction to Systems 

Thinking. 

50


relationships with each other, the flow of the process, and phenomena that 

influence the elements and the flow. The model uses four icons to represent 

actions and relationships within the system: stocks, flows, converters, and 

connectors. The icons and their placement within the systems model show the 

relationships of the elements of the analyzed phenomenon. 

The Components of the Systems Model 

Icon 

Stock 

Flow 

Converter 

Flow 

Stock 

Purpose 

Stocks represent accumulations. These are quantities 

that can increase or decrease, such as the amount of 

work that needs to be completed, the time available in 

which to do it, experience one might bring to a task. 

Flows represent activities. They control the filling or 

draining of stocks, causing conditions to change. 

Converters change inputs into outputs. They usually 

represent the variables that initiate change. In the 

example, a converter might represent a sudden and 

drastic world event. 

Connectors link elements to other elements, 

representing assumptions about what depends on 

what. 

Converter 

The systems model of the Intelligence Cycle provides insights into the process 

of analysis as well as other factors that can influence the successful and timely 

completion of an intelligence task. It also provides a way to understand the 

impact of change in any area of the Intelligence Cycle on other elements, either 

through reflection or by applying mathematical values to the influences and relationships 

and running simulations of the model. 

Demand. As in the traditional Intelligence Cycle model, the systems model 

begins with requirements for information that generally come from policymakers. 

These requirements are represented by a stock (found in the upper 

left-hand quarter of the diagram) because they can increase or decrease based 

on the level of need for information (a flow). The change in level of need is 

influenced by national and world events, as well as by new questions or 

requests for clarification of items in previously delivered products. Each 

request does not contribute equally to the amount of work, which is influenced 

by the types of documents or products requested, the complexity of the prod- 

51

CHAPTER FOUR 

Systems Model of the Intelligence Cycle 

52


ucts, and the turnaround time imposed. All of these factors determine the level 

of demand placed on the analyst. 

Production. This section focuses on the process of producing intelligence 

products. The elements described are tied, directly or indirectly, to the flow 

that represents changes in the analyst’s ability to produce. In turn, these 

changes cause products to be completed and requests of policymakers to be 

fulfilled. It is important to note that this portion of the model deals with factors 

that influence the act of analysis and does not attempt to address methods of 

analysis. 

Factors that influence the ability of analysts to produce are numerous and 

complex, as shown. First and foremost are the capabilities an analyst brings to 

the task. This is represented by a stock—usually an increasing one—that 

derives from an analyst’s education, training, and experience. 

Another influence is the number and frequency of evaluations and revisions 

imposed on a work in progress. That a draft of the product must be reviewed 

and edited by a number of others places variable constraints on the time available 

for creating the original draft. This factor increases in significance when 

the product requested has a short deadline. 

Political and cultural values of the organization also have an influence, usually 

constraining. Strictly following traditional heuristics and methods and 

meeting organizational or management expectations may influence both an 

analyst’s ability to produce and the quality of the output. The weight of these 

influences will vary depending on the experience of the analyst. 

Another factor that influences the analyst’s ability to produce is the amount 

of relevant, usable data (a stock) available. The term “relevant, usable data” 

describes all collected intelligence that is relevant to meeting the request and 

that exists in a format that can be used to develop the product. To become 

usable, the data must go through steps that are influenced by a variety of other 

people, organizations, systems, and technologies. This process is represented 

by the stock and flow chain that appears across the middle of diagram. 

Data are collected from a variety of sources, represented by the INTs converter. 

10 These data add to the stock of collected data. The ways in which 

accumulated collected data are converted to the stock of available data are 

influenced by internal research demands and specific collection requirements 

imposed by analysts, policymakers, and others. Once the data are processed 

and put into an agreed format for use by intelligence producers and consum- 

10 

INT is an abbreviation for intelligence, usually contained in acronyms for the various types of 

intelligence collected by the Intelligence Community, for example, HUMINT (human intelligence) 

and SIGINT (signals intelligence). 

53

CHAPTER FOUR 

ers, they add to the accumulation of material that affects the ability of an analysts 

to produce. 

Product Influences. The accumulation of completed intelligence products, 

which is represented as a stock, is not in practice an end-state for analysis. A 

customer may respond to a delivered product by levying additional or revised 

tasking. In all instances, this information influences the level of need for policymaker 

requirements and causes the process to begin again. Each iteration of 

the process is different, not because the steps in the process change, but 

because those responsible for carrying out the steps have changed as a result 

of their participation in the previous run. These changes can include a greater 

level of experience with the process, with the customer, with the topic area, or 

with the quirks of the organization and its processes. The changes are a manifestation 

of the concept that the system is greater than the sum of its parts. 

Findings Based on Systems Analysis 

Systems analysis clearly demonstrates the defects of the traditional Intelligence 

Cycle model. To recapitulate briefly, the traditional model merely represents 

a simple list of steps rather than a dynamic closed feedback loop. In 

addition, although the steps are meant to be performed by several different 

actors, the model does not provide useful information about what each actually 

contributes to the cycle, nor does it accurately represent the path a request 

takes as it is addressed. Another problem with the traditional model is that 

none of its features help identify ways of developing a consistent product. For 

example, there is no allowance for a statement of objectives or for any formative 

or summative evaluations to check that objectives have been met. 

On the other hand, the model that resulted from a systems analysis provides 

a more complex view. That model shows cause and effect, and it shows what 

other elements have an impact on the development of intelligence products 

and how and why elements depend on other elements. These advantages of the 

systems model are clearly apparent in considering the role of analysts in production, 

a crucial element of the cycle that the traditional model all but 

ignores. 

Impact on Production and Analyst’s Control. Study of the systems model 

shows that the “Analyst’s Ability to Produce” (upper right-hand quarter of the 

diagram) is the central factor in the production cycle and the driver of the feedback 

loop. The systems view also makes us aware of a less obvious fact that is 

critically important to a discussion of analytic failure. 

A look at the entire system makes readily apparent the number of factors of 

varying complexity that influence an analyst’s ability to produce: the analyst’s 

capabilities; the product evaluation process; the political and cultural values of 

54


the organization; the amount of relevant, usable data and actions related to 

transforming collected data to relevant, usable data; and the level of demand 

on the analyst. Of these five factors, only one—the analyst’s capabilities—is 

an internal factor and somewhat under the analyst’s control. 11 Yet, even 

though the other factors are out of the analyst’s control, the analyst must rely 

on them to accomplish the goal and to meet the expectations of customers and 

the organization. When the proportion of external factors to internal factors is 

as unbalanced as the systems model of the Intelligence Cycle demonstrates, 

the causes of stress in the analytic environment increase, as does the possibility 

that stress will occur. 

In such a high stress environment, where the critical person is responsible 

for delivering a product whose development relies on a great number of factors 

beyond his or her control, there is greater risk of error, with an increased 

likelihood of incomplete or incorrect products. Tendencies to use shortcuts, to 

avoid creative thinking, and to minimize the perceived impact of certain 

events or actions become more apparent in this situation, especially if their 

implementation means reducing the workload and the stressors. Results of 

working in such an environment can include increased personnel turnover, 

missed or undervalued information, lack of attention to detail, decreased motivation, 

and a lack of creativity in approaching analysis. Moreover, with analysts 

so central to the process, their actions may have a widespread and, thus, 

powerful influence on the entire system. This change can be positive or negative. 

Given the number of elements influencing the analyst that are out of his 

or her control, however, it is unlikely that the changes would positively affect 

the quality, accuracy, and number of intelligence products created. 

Recommendations 

Revisit the traditional intelligence model. The traditional Intelligence Cycle 

model should either be redesigned to depict accurately the intended goal, or 

care should be taken to discuss explicitly its limitations whenever it is used. 

Teaching with an inaccurate aid merely leads to misconceptions that can result 

in poor performance, confusion, and a need for unlearning and reteaching. If 

the objective is to capture the entire intelligence process, from the request for 

a product to its delivery, including the roles and responsibilities of Intelligence 

Community members, then something more is required. This should be a 

model that pays particular attention to representing accurately all the elements 

of the process and the factors that influence them. 

11 

Even the factors that contribute to the analyst’s capabilities, notably experience and training, may 

be seen to be under the control of others when access to, and selection of, them are considered. 

55

CHAPTER FOUR 

Further Study. The use of simulation allows us to determine flaws in the system 

that basic informational models cannot address. A simulation moves the 

image of the Intelligence Cycle from a picture that selectively and indiscriminately 

illustrates a series of events to a holistic and realistic representation of 

events, responsibilities, processes, and their impact on each other. The simulation 

of the Intelligence Cycle developed for this analysis is merely a first step. 

Further work should be done with it to validate the representations, test for vulnerabilities, 

predict outcomes, and accurately recommend changes. 

Lightening the Analyst’s Load. The systems model reveals a serious imbalance 

in the work processes analysts can and cannot control. It is unrealistic 

and unnecessary to consider reorganizing the process to correct this defect. 

However, there are actions that could be taken to provide analysts more control 

over external factors without significantly altering their roles. These 

actions would also reduce the amount of potential influence that one group 

could have over the entire process. 

First, analysts might be designated as reports or research analysts. The 

former would prepare products that address short-term tasks, such as writing 

for the PDB. As the process of collection and analysis is different for shortand 

long-term products, this might be a responsibility assigned primarily to 

more junior analysts. Research analysts might be those with more experience. 

Freed from the obligation to prepare short-term reports, senior analysts would 

be available for more intense research efforts, such as those required for an 

NIE. In addition, cross-training or experience in creating both products and 

the flexibility to switch from one process to another would provide greater 

depth of personnel. If appropriate, movement to a long-term research position 

could be viewed as professional development. 

Second, personnel responsible for formatting and processing raw data 

might be included on accounts. Through association with a particular group, 

people in this role would have a reasonable idea of analysts’ requirements. 

This would allow the preselection and preparation of data, so that analysts 

could focus on “connecting the dots.” The skills requirement for this role 

would be akin to those of a research librarian. 

Third, tools to help the analyst identify, manage, and fuse relevant data 

could be identified and deployed. These tools, which need not be limited to 

those that are technology-based, should be used to support analysts’ laborintensive 

tasks, thereby freeing them to focus on the analysis of data. 

Employ alternative methods for examining work processes. Just as we used 

alternative methods to examine the Iintelligence Cycle, and as managers press 

analysts to use alternative analyses in assessing their targets, so should managers 

employ alternative methods for examining work processes. These methods 

56


should not simply test effectivenss; they should also identify vulnerabilities and 

potential sources of other problems in the community’s analytical methods. 

57

PART III 

Potential Areas for Improvement 

59

CHAPTER FIVE 

Integrating Methodologists into Teams of Experts 1 

Intelligence analysis, like other complex tasks, demands considerable 

expertise. It requires individuals who can recognize patterns in large data sets, 

solve complex problems, and make predictions about future behavior or 

events. To perform these tasks successfully, analysts must dedicate years to 

researching specific topics, processes, and geographic regions. 

Paradoxically, it is the specificity of expertise that makes expert forecasts 

unreliable. While experts outperform novices and machines in pattern recognition 

and problem solving, expert predictions of future behavior or events are 

seldom as accurate as Bayesian probabilities. 2 This is due, in part, to cognitive 

biases and processing-time constraints and, in part, to the nature of expertise 

itself and the process by which one becomes an expert. 

Becoming an Expert 

Expertise is commitment coupled with creativity. By this, I mean the commitment 

of time, energy, and resources to a relatively narrow field of study 

and the creative energy necessary to generate new knowledge in that field. It 

takes a great deal of time and regular exposure to a large number of cases to 

become an expert. 

1 

A version of this chapter originally appeared as “Integrating Methodologists into Teams of Substantive 

Experts in Studies in Intelligence 47, no. 1 (2003): 57–65. 

2 

Method for estimating the probability of a given outcome developed by Thomas Bayes (1702– 

61), an English mathematician. See Thomas Bayes, “An Essay Toward Solving a Problem In the 

Doctrine of Chances.” 

61

CHAPTER FIVE 

Entering a field of study as a novice, an individual needs to learn the heuristics 

and constraints—that is, the guiding principles and rules—of a given task 

in order to perform that task. Concurrently, the novice needs to be exposed to 

specific cases that test the reliability of such heuristics. Generally, novices 

find mentors to guide them through the process of acquiring new knowledge. 

A fairly simple example would be someone learning to play chess. The novice 

chess player seeks a mentor who can explain the object of the game, the number 

of spaces, the names of the pieces, the function of each piece, how each 

piece is moved, and the necessary conditions for winning or losing a game. 

In time, and with much practice, the novice begins to recognize patterns of 

behavior within cases and, thus, becomes a journeyman. With more practice 

and exposure to increasingly complex cases, the journeyman finds patterns not 

only within but also among cases and, more important, learns that these patterns 

often repeat themselves. Throughout, the journeyman still maintains regular 

contact with a mentor to solve specific problems and to learn more 

complex strategies. Returning to the example of the chess player, the individual 

begins to learn patterns of opening moves, offensive and defensive strategies, 

and patterns of victory and defeat. 

The next stage begins when a journeyman makes and tests hypotheses 

about future behavior based on past experiences. Once he creatively generates 

knowledge, rather than simply matching patterns, he becomes an expert. At 

this point, he becomes responsible for his own knowledge and no longer needs 

a mentor. In the chess example, once a journeyman begins competing against 

experts, makes predictions based on patterns, and tests those predictions 

against actual behavior, he is generating new knowledge and a deeper understanding 

of the game. He is creating his own cases rather than relying on the 

cases of others. 

The chess example in the preceding paragraphs is a concise description of 

an apprenticeship model. Apprenticeship may seem to many a restrictive, oldfashioned 

mode of education, but it remains a standard method of training for 

many complex tasks. In fact, academic doctoral programs are based on an 

apprenticeship model, as are such fields as law, music, engineering, and medicine. 

Graduate students enter fields of study, find mentors, and begin the long 

process of becoming independent experts and generating new knowledge in 

their respective domains. 

To some, playing chess may appear rather trivial when compared, for 

example, with making medical diagnoses, but both are highly complex tasks. 

Chess heuristics are well-defined, whereas medical diagnoses seem more open 

ended and variable. In both instances, however, there are tens of thousands of 

potential patterns. A research study discovered that chess masters had spent 

between 10,000 and 20,000 hours, or more than 10 years, studying and play- 

62

INTEGRATING METHODOLOGISTS 

ing chess. On average, a chess master acquires 50,000 different chess patterns. 

3 

Similarly, a diagnostic radiologist spends eight years in full-time medical 

training - four years of medical school and four years of residency—before 

being qualified to take a national board exam and begin independent practice. 4 

According to a 1988 study, the average diagnostic radiology resident sees 40 

cases per day, or around 12,000 cases per year. 5 At the end of a residency, a 

diagnostic radiologist has acquired an average of 48,000 cases. 

Psychologists and cognitive scientists agree that the time it takes to become 

an expert depends on the complexity of the task and the number of cases, or 

patterns, to which an individual is exposed. The more complex the task, the 

longer it takes to build expertise, or, more accurately, the longer it takes to 

experience a large number of cases or patterns. 

The Power of Expertise 

Experts are individuals with specialized knowledge suited to perform the 

specific tasks for which they are trained, but that expertise does not necessarily 

transfer to other domains. 6 A master chess player cannot apply chess 

expertise in a game of poker; although both chess and poker are games, a 

chess master who has never played poker is a novice poker player. Similarly, a 

biochemist is not qualified to perform neurosurgery, even though both biochemists 

and neurosurgeons study human physiology. In other words, the 

more complex a task, the more specialized and exclusive is the knowledge 

required to perform that task. 

Experts perceive meaningful patterns in their domains better than do nonexperts. 

Where a novice perceives random or disconnected data points, an 

expert connects regular patterns within and among cases. This ability to identify 

patterns is not an innate perceptual skill; rather, it reflects the organization 

of knowledge after exposure to and experience with thousands of cases. 7 

Experts have a deeper understanding of their domains than do novices, and 

they utilize higher-order principles to solve problems. 8 A novice, for example, 

might group objects together by color or size, whereas an expert would group 

3 

W. Chase and H. Simon, “Perception in Chess.” 

4 

American College of Radiology. Personal communication, 2002. 

5 

A. Lesgold et al., “Expertise in a Complex Skill: Diagnosing X-Ray Pictures.” 

6 

M. Minsky and S. Papert, Artificial Intelligence; J. Voss and T. Post, “On the Solving of Ill- 

Structured Problems.” 

7 

O. Akin, Models of Architectural Knowledge; D. Egan and B. Schwartz, “Chunking in Recall of 

Symbolic Drawings”; K. McKeithen et al., “Knowledge Organization and Skill Differences in 

Computer Programmers.” 

63

CHAPTER FIVE 

the same objects according to their function or utility. Experts comprehend the 

meaning of data better than novices, and they weigh variables with different 

criteria within their domains better. Experts recognize variables that have the 

largest influence on a particular problem and focus their attention on those 

variables. 

Experts have better domain-specific short-term and long-term memory than 

do novices. 9 Moreover, experts perform tasks in their domains faster than novices 

and commit fewer errors while solving problems. 10 Interestingly, experts 

also go about solving problems differently. At the beginning of a task, experts 

spend more time thinking about a problem than do novices, who immediately 

seek to find a solution. 11 Experts use their knowledge of previous cases as context 

for creating mental models to solve given problems. 12 

Because they are better at self-monitoring than novices, experts are more 

aware of instances where they have committed errors or failed to understand a 

problem. 13 They check their solutions more often and recognize when they 

are missing information necessary for solving a problem. 14 Experts are aware 

of the limits of their knowledge and apply their domain’s heuristics to solve 

problems that fall outside of their experience base. 

The Paradox of Expertise 

The strengths of expertise can also be weaknesses. 15 Although one would 

expect experts to be good forecasters, they are not particularly good at it. 

Researchers have been testing the ability of experts to make forecasts since 

the 1930s. 16 The performance of experts has been tested against Bayesian 

probabilities to determine if they are better at making predictions than simple 

statistical models. Seventy years later, after more than 200 hundred experiments 

in different domains, it is clear that the answer is no. 17 Supplied with an 

equal amount of data about a particular case, Bayesian probability data are as 

8 

M. Chi, P. Feltovich, and R. Glaser, “Categorization and Representation of Physics Problems by 

Experts and Novices”; M. Weiser and J. Shertz, “Programming Problem Representation in Novice 

and Expert Programmers.” 

9 

W. Chase and K. Ericsson, “Skill and Working Memory.” 

10 

W. Chase, “Spatial Representations of Taxi Drivers.” 

11 

J. Paige and H. Simon, “Cognition Processes in Solving Algebra Word Problems.” 

12 

Voss and Post. 

13 

M. Chi, R. Glaser, and E. Rees, “Expertise in Problem Solving”; D. Simon and H. Simon, 

“Individual Differences in Solving Physics Problems.” 

14 

J. Larkin, “The Role of Problem Representation in Physics.” 

15 

C. Camerer and E. Johnson, “The Process-Performance Paradox in Expert Judgment.” 

16 

H. Reichenbach, Experience and Prediction; T. Sarbin, “A Contribution to the Study of Actuarial 

and Individual Methods of Prediction.” 

64


good as, or better than, an expert at making calls about the future. In fact, the 

expert does not tend to outperform the actuarial table, even if given more specific 

case information than is available to the statistical model. 18 

There are few exceptions to these research findings, but these are informative. 

When experts are given the results of the Bayesian probabilities, for 

example, they tend to score as well as the statistical model if they use the statistical 

information in making their own predictions. 19 In addition, if experts 

have privileged information that is not reflected in the statistical table, they 

will actually perform better than does the table. A classic example is the case 

of the judge’s broken leg. Judge X has gone to the theater every Friday night 

for the past 10 years. Based on a Bayesian analysis, one would predict, with 

some certainty, that this Friday night would be no different. An expert knows, 

however, that the judge broke her leg Thursday afternoon and is expected to 

be in the hospital until Saturday. Knowing this key variable allows the expert 

to predict that the judge will not attend the theater this Friday. 

Although having a single variable as the determining factor makes this case 

easy to grasp, analysis is seldom, if ever, this simple. Forecasting is a complex, 

interdisciplinary, dynamic, and multivariate task wherein many variables 

interact, weight and value change, and other variables are introduced or omitted. 

During the past 30 years, researchers have categorized, experimented, and 

theorized about the cognitive aspects of forecasting and have sought to 

explain why experts are less accurate forecasters than statistical models. 

Despite such efforts, the literature shows little consensus regarding the causes 

or manifestations of human bias. Some have argued that experts, like all 

humans, are inconsistent when using mental models to make predictions. That 

is, the model an expert uses for predicting X in one month is different from the 

model used for predicting X in a later month, although precisely the same case 

and same data set are used in both instances. 20 A number of researchers point 

17 

R. Dawes, D. Faust, and P. Meehl, “Clinical Versus Actuarial Judgment”; W. Grove and P. 

Meehl, “Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, 

Algorithmic) Prediction Procedures.” 

18 

R. Dawes, “A Case Study of Graduate Admissions”; Grove and Meehl; H. Sacks, “Promises, 

Performance, and Principles”; T. Sarbin, “A Contribution to the Study of Actuarial and Individual 

Methods of Prediction”; J. Sawyer, “Measurement and Prediction, Clinical and Statistical”; W. 

Schofield and J. Garrard, “Longitudinal Study of Medical Students Selected for Admission to 

Medical School by Actuarial and Committee Methods.” 

19 

L. Goldberg, “Simple Models or Simple Processes?”; L. Goldberg, “Man versus Model of 

Man”; D. Leli and S. Filskov, “Clinical-Actuarial Detection of and Description of Brain Impairment 

with the Wechsler-Bellevue Form I.” 

20 

J. Fries, et al., “Assessment of Radiologic Progression in Rheumatoid Arthritis.” 

65

CHAPTER FIVE 

to human biases to explain unreliable expert predictions. 21 There is general 

agreement that two types of bias exist: 

• Pattern bias: looking for evidence that confirms rather than rejects a 

hypothesis and/or filling in—perhaps inadvertently—missing data with 

data from previous experiences; 

• Heuristic bias: using inappropriate guidelines or rules to make predictions. 

Paradoxically, the very method by which one becomes an expert explains 

why experts are much better than novices at describing, explaining, performing 

tasks, and solving problems within their domains but, with few exceptions, 

are worse at forecasting than are Bayesian probabilities based on 

historical, statistical models. A given domain has specific heuristics for performing 

tasks and solving problems, and these rules are a large part of what 

makes up expertise. In addition, experts need to acquire and store tens of thousands 

of cases in order to recognize patterns, generate and test hypotheses, and 

contribute to the collective knowledge within their fields. In other words, 

becoming an expert requires a significant number of years of viewing the 

world through the lens of one specific domain. This concentration gives the 

expert the power to recognize patterns, perform tasks, and solve problems, but 

it also focuses the expert’s attention on one domain to the exclusion of others. 

It should come as little surprise, then, that an expert would have difficulty 

identifying and weighing variables in an interdisciplinary task, such as forecasting 

an adversary’s intentions. Put differently, an expert may know his specific 

domain, such as economics or leadership analysis, quite thoroughly, but 

that may still not permit him to divine an adversary’s intention, which the 

adversary may not himself know. 

The Burden on Intelligence Analysts 

Intelligence analysis is an amalgam of a number of highly specialized 

domains. Within each, experts are tasked with assembling, analyzing, assigning 

meaning to, and reporting on data, the goals being to describe an event or 

observation, solve a problem, or make a forecast. Experts who encounter a 

case outside their field repeat the steps they initially used to acquire their 

expertise. Thus, they can try to make the new data fit a pattern previously 

acquired; recognize that the case falls outside their expertise and turn to their 

domain’s heuristics to try to give meaning to the data; acknowledge that the 

21 

J. Evans, Bias in Human Reasoning; R. Heuer, Psychology of Intelligence Analysis; D. Kahneman, 

P. Slovic, and A. Tversky, Judgment Under Uncertainty; A. Tversky and D. Kahneman, 

“The Belief in the ‘Law of Small Numbers’.” 

66


case still does not fit with their expertise and reject the data set as an anomaly; 

or consult other experts. 

An item of information, in and of itself, is not domain specific. Imagine 

economic data that reveal that a country is investing in technological infrastructure, 

chemical supplies, and research and development. An economist 

might decide that the data fit an existing spending pattern and integrate these 

facts with prior knowledge about a country’s economy. The same economist 

might decide that this is a new pattern that needs to be stored in long-term 

memory for some future use, or he might decide that the data are outliers of no 

consequence and may be ignored. Finally, the economist might decide that the 

data would be meaningful to a chemist or biologist and, therefore, seek to collaborate 

with other specialists, who might reach different conclusions regarding 

the data than would the economist. 

In this example, the economist is required to use his economic expertise in 

all but the final option of consulting other experts. In the decision to seek collaboration, 

the economist is expected to know that what appears to be new 

economic data may have value to a chemist or biologist, domains with which 

he may have no experience. In other words, the economist is expected to know 

that an expert in some other field might find meaning in data that appear to be 

economic. 

Three disparate variables complicate the economist’s decisionmaking: 

• Time context. This does not refer to the amount of time necessary to 

accomplish a task but rather to the limitations that come from being close 

to an event. The economist cannot say a priori that the new data set is the 

critical data set for some future event. In “real time,” they are simply data 

to be manipulated. It is only in retrospect, or in long-term memory, that 

the economist can fit the data into a larger pattern, weigh their value, and 

assign them meaning. 

• Pattern bias. In this particular example, the data have to do with infrastructure 

investment, and the expert is an economist. Thus, it makes perfect 

sense to try to manipulate the new data within the context of 

economics, recognizing, however, that there may be other, more important 

angles. 

• Heuristic bias. The economist has spent a career becoming familiar with 

and using the guiding principles of economic analysis and, at best, has 

only a vague familiarity with other domains and their heuristics. An economist 

would not necessarily know that a chemist or biologist could identify 

what substance is being produced based on the types of equipment 

and supplies that are being purchased. 

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CHAPTER FIVE 

This example does not describe a complex problem; most people would 

recognize that the data from this case might be of value to other domains. It is 

one isolated case, viewed retrospectively, which could potentially affect two 

other domains. But, what if the economist had to deal with 100 data sets per 

day? Now, multiply those 100 data sets by the number of domains potentially 

interested in any given economic data set. Finally, put all of this in the context 

of “real time.” The economic expert is now expected to maintain expertise in 

economics, which is a full-time endeavor, while simultaneously acquiring 

some level of experience in every other domain. Based on these expectations, 

the knowledge requirements for effective collaboration quickly exceed the 

capabilities of the individual expert. 

The expert is left dealing with all of these data through the lens of his own 

expertise. Let’s assume that he uses his domain heuristics to incorporate the 

data into an existing pattern, store the data in long-term memory as a new pattern, 

or reject the data set as an outlier. In each of these options, the data stop 

with the economist instead of being shared with an expert in some other 

domain. The fact that these data are not shared then becomes a potentially critical 

case of analytic error. 22 

In hindsight, critics will say that the implications were obvious—that the 

crisis could have been avoided if the data had been passed to one or another 

specific expert. In “real time,” however, an expert often does not know which 

particular data set would have value for an expert in another domain. 

The Pros and Cons of Teams 

One obvious solution to the paradox of expertise is to assemble an interdisciplinary 

team. Why not simply make all problem areas or country-specific 

data available to a team of experts from a variety of domains? This ought, at 

least, to reduce the pattern and heuristic biases inherent in relying on only one 

domain. Ignoring potential security issues, there are practical problems with 

this approach. First, each expert would have to sift through large data sets to 

find data specific to his expertise. This would be inordinately time-consuming 

and might not even be routinely possible, given the priority accorded gisting 

and current reporting. 

Second, during the act of scanning large data sets, the expert inevitably 

would be looking for data that fit within his area of expertise. Imagine a chemist 

who comes across data that show that a country is investing in technologi- 

22 

L. Kirkpatrick, Captains Without Eyes: Intelligence Failures in World War II; F. Shiels, Preventable 

Disasters; J. Wirtz, The Tet Offensive: Intelligence Failure in War; R. Wohlstetter, Pearl 

Harbor. 

68


cal infrastructure, chemical supplies, and research and development (the same 

data that the economist analyzed in the previous example). The chemist recognizes 

that these are the ingredients necessary for a nation to produce a specific 

chemical agent, which could have a military application or could be benign. 

The chemist then meshes the data with an existing pattern, stores the data as a 

new pattern, or ignores the data as an anomaly. 

The chemist, however, has no frame of reference regarding spending trends 

in the country of interest. He does not know if the investment in chemical supplies 

represents an increase, a decrease, or a static spending pattern—answers 

the economist could supply immediately. There is no reason for the chemist to 

know if a country’s ability to produce this chemical agent is a new phenomenon. 

Perhaps the country in question has been producing the chemical agent 

for years, and these data are part of some normal pattern of behavior. 

If this analytic exercise is to begin to coalesce, neither expert must treat the 

data set as an anomaly and both must report it as significant. In addition, each 

expert’s analysis of the data—an increase in spending and the identification of 

a specific chemical agent—must be brought together at some point. The problem 

is, at what point? Presumably, someone will get both of these reports 

somewhere along the intelligence chain. Of course, the individual who gets 

these reports will be subject to the same three complicating variables 

described earlier—time context, pattern bias, and heuristic bias—and may not 

be able to synthesize the information. Thus, the burden of putting the pieces 

together will merely have been shifted to someone else in the organization. 

In order to avoid shifting the problem from one expert to another, an actual 

collaborative team could be built. Why not explicitly put the economist and 

the chemist together to work on analyzing data? The utilitarian problems with 

this strategy are obvious: not all economic problems are chemical, and not all 

chemical problems are economic. Each expert would waste an inordinate 

amount of time. Perhaps one case in 100 would be applicable to both experts, 

but, during the rest of the day, they would drift back to their individual 

domains, in part, because that is what they are best at and, in part, just to stay 

busy. 

Closer to the real world, the same example may also have social, political, 

historical, and cultural aspects. Despite an increase in spending on a specific 

chemical agent, the country in question may not be inclined to use it in a 

threatening way. For example, there may be social data unavailable to the 

economist or the chemist indicating that the chemical agent will be used for a 

benign purpose. In order for collaboration to work, each team would have to 

have experts from many domains working together on the same data set. 

Successful teams have very specific organizational and structural requirements. 

An effective team requires discrete and clearly stated goals that are 

69

CHAPTER FIVE 

shared by each team member. 23 Teams also require interdependence and 

accountability, that is, the success of each individual depends on the success of 

the team as a whole as well as on the individual success of every other team 

member. 24 

Effective teams require cohesion, formal and informal communication, 

cooperation, shared mental models, and similar knowledge structures. 25 Putting 

combinations such as this in place is not a trivial task. Creating shared 

mental models may be fairly easy within an air crew or a tank crew, where an 

individual’s role is clearly identifiable as part of a clearly-defined, repetitive 

team effort, such as landing a plane or acquiring and firing on a target. It is 

more difficult within an intelligence team, given the vague nature of the goals, 

the enormity of the task, and the diversity of individual expertise. Moreover, 

the larger the number of team members, the more difficult it is to generate 

cohesion, communication, and cooperation. Heterogeneity can also be a challenge; 

it has a positive effect on generating diverse viewpoints within a team, 

but it requires more organizational structure than does a homogeneous team. 26 

Without specific processes, organizing principles, and operational structures, 

interdisciplinary teams will quickly revert to being simply a room full of 

experts who ultimately drift back to their previous work patterns. That is, the 

experts will not be a team at all; they will be a group of experts individually 

working in some general problem space. 27 

23 

Dorwin Cartwright and Alvin Zander, Group Dynamics: Research and Theory; P. Fandt, W. 

Richardson, and H. Conner, “The Impact of Goal Setting on Team Simulation Experience”; J. 

Harvey and C. Boettger, “Improving Communication within a Managerial Workgroup.” 

24 

M. Deutsch, “The Effects of Cooperation and Competition Upon Group Process”; D. Johnson 

and R. Johnson, “The Internal Dynamics of Cooperative Learning Groups”; D. Cartwright and A. 

Zander, Group Dynamics: Research and Theory; David Johnson and Roger Johnson, “The Internal 

Dynamics of Cooperative Learning Groups”; D. Johnson et al., “Effects of Cooperative, Competitive, 

and Individualistic Goal Structure on Achievement: A Meta-Analysis”; R. Slavin, 

“Research on Cooperative Learning”; R. Slavin, Cooperative Learning. 

25 

J. Cannon-Bowers, E. Salas, S. Converse, “Shared Mental Models in Expert Team Decision 

Making”; L. Coch and J. French, “Overcoming Resistance to Change”; M. Deutsch, “The Effects 

of Cooperation and Competition Upon Group Process”; L. Festinger, “Informal Social Communication”; 

D. Johnson et al., “The Impact of Positive Goal and Resource Interdependence on 

Achievement, Interaction, and Attitudes”; B. Mullen and C. Copper, “The Relation Between 

Group Cohesiveness and Performance: An Integration”; W. Nijhof and P. Kommers, “An Analysis 

of Cooperation in Relation to Cognitive Controversy”; J. Orasanu, “Shared Mental Models 

and Crew Performance”; S. Seashore, Group Cohesiveness in the Industrial Work-group. 

26 

T. Mills, “Power Relations in Three-Person Groups”; L. Molm, “Linking Power Structure and 

Power Use”; V. Nieva, E. Fleishman, and A. Rieck, Team Dimensions: Their Identity, Their Measurement, 

and Their Relationships; G. Simmel, The Sociology of Georg Simmel. 

27 

R. Johnston, Decision Making and Performance Error in Teams: Research Results; J. Meister, 

“Individual Perceptions of Team Learning Experiences Using Video-Based or Virtual Reality 

Environments.” 

70


Can Technology Help? 

There are potential technological alternatives to multifaceted teams. For 

example, an Electronic Performance Support System (EPSS) is a large database 

that is used in conjunction with expert systems, intelligent agents, and 

decision aids. 28 Although applying such a system to intelligence problems 

might be a useful goal, at present, the notion of an integrated EPSS for large 

complex data sets is more theory than practice. 29 In addition to questions 

about the technological feasibility of such a system, there are fundamental 

epistemological challenges. It is virtually inconceivable that a comprehensive 

computational system could bypass the three complicating variables of expertise 

described earlier. 

An EPSS, or any other computational solution, is designed, programmed, 

and implemented by a human expert from one domain only, that of computer 

science. Historians will not design the “historical decision aid,” economists 

will not program the “economic intelligent agent,” chemists will not create the 

“chemical agent expert system.” Computer scientists may consult with various 

experts during the design phase of such a system, but, when it is time to sit 

down and write code, the programmer will follow the heuristics with which he 

is familiar. 30 In essence, one would be trading the heuristics of dozens of 

domains for those that govern computer science. This would reduce the problem 

of processing time by simplifying and linking data, and it might reduce 

pattern bias. It would not reduce heuristic bias, however; if anything, it might 

exaggerate it by reducing all data to a binary state. 31 

This skepticism is not simply a Luddite reaction to technology. Computational 

systems have had a remarkable, positive effect on processing time, storage, 

and retrieval. They have also demonstrated utility in identifying patterns 

within narrowly defined domains. However, intelligence analysis requires the 

expertise of so many diverse fields of study and is not something a computational 

system handles well. Although an EPSS, or some other form of computational 

system, may be a useful tool for manipulating data, it is not a solution 

to the paradox of expertise. 

28 

An Expert System is a job-specific heuristic process that helps an expert narrow the range of 

available choices. An Intelligent Agent is an automated program (bot) with built-in heuristics used 

in Web searches. A Decision Aid is an expert system whose scope is limited to a particular task. 

29 

R. Johnston, “Electronic Performance Support Systems and Information Navigation.” 

30 

R. Johnston and J. Fletcher, A Meta-Analysis of the Effectiveness of Computer-Based Training 

for Military Instruction. 

31 

J. Fletcher and R. Johnston, “Effectiveness and Cost Benefits of Computer-Based Decision 

Aids for Equipment Maintenance.” 

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CHAPTER FIVE 

Analytic Methodologists 

Most domains have specialists who study the scientific process or research 

methods of their discipline. Instead of specializing in a specific substantive 

topic, these experts specialize in mastering the research and analytic methods 

of their domain. In the biological and medical fields, these methodological 

specialists are epidemiologists. In education and public policy, they are program 

evaluators. In other fields, they are research methodologists or statisticians. 

Whatever the label, each field recognizes that it requires experts in 

methodology who focus on deriving meaning from data, recognizing patterns, 

and solving problems within a domain in order to maintain and pass on the 

domain’s heuristics. They become in-house consultants—organizing agents— 

who work to identify research designs, methods for choosing samples, and 

tools for data analysis. 

Because they have a different perspective than do the experts in a domain, 

methodologists are often called on by substantive experts to advise them on a 

variety of process issues. On any given day, an epidemiologist, for example, 

may be asked to consult on studies of the effects of alcoholism or the spread of 

a virus on a community or to review a double-blind clinical trial of a new 

pharmaceutical product. In each case, the epidemiologist is not being asked 

about the content of the study; rather, he is being asked to comment on the 

research methods and data analysis techniques used. 

Although well over 160 analytic methods are available to intelligence analyst, 

few methods specific to the domain of intelligence analysis exist. 32 Intelligence 

analysis has few specialists whose professional training is in the 

process of employing and unifying the analytic practices within the field. It is 

left to the individual analysts to know how to apply methods, select one 

method over another, weigh disparate variables, and synthesize the results— 

the same analysts whose expertise is confined to specific substantive areas and 

their own domains’ heuristics. 

Conclusion 

Intelligence agencies continue to experiment with the right composition, 

structure, and organization of analytic teams. Yet, although they budget significant 

resources for technological solutions, comparatively little is being 

32 

Exceptions include: S. Feder, “FACTIONS and Policon”; R. Heuer, Psychology of Intelligence 

Analysis; R. Hopkins, Warnings of Revolution: A Case Study of El Salvador; J. Lockwood and K. 

Lockwood, “The Lockwood Analytical Method for Prediction (LAMP)”; J. Pierce, “Some Mathematical 

Methods for Intelligence Analysis”; E. Sapp, “Decision Trees”; J. Zlotnick, “Bayes’ 

Theorem for Intelligence Analysis.” 

72


done to advance methodological science. Methodological improvements are 

left primarily to the individual domains, a practice that risks falling into the 

same paradoxical trap that currently exists. What is needed is an intelligencecentric 

approach to methodology that will include the methods and procedures 

of many domains and the development of heuristics and techniques unique to 

intelligence. In short, intelligence analysis needs its own analytic heuristics 

that are designed, developed, and tested by professional analytic methodologists. 

The desired outcome would be a combined approach that includes formal 

thematic teams with structured organizational principles, technological systems 

designed with significant input from domain experts, and a cadre of analytic 

methodologists. These methodologists would act as in-house consultants 

for analytic teams, generate new methods specific to intelligence analysis, 

modify and improve existing methods of analysis, and promote the professionalization 

of the discipline of intelligence. Although, at first, developing a 

cadre of analytic methodologists would require using specialists from a variety 

of other domains and professional associations, in time, the discipline 

would mature into its own subdiscipline with its own measures of validity and 

reliability. 

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CHAPTER SIX 

The Question of Foreign Cultures: Combating 

Ethnocentrism in Intelligence Analysis 

The intelligence literature often cautions intelligence professionals to be 

wary of mirror imaging. 1 Although the term is a misnomer (a mirror image is 

a reverse image), the concept is that individuals perceive foreigners—both 

friends and adversaries of the United States—as thinking the same way as 

Americans. 2 Individuals do, in fact, have a natural tendency to assume that 

others think and perceive the world in the same way they do. This type of projective 

identification, or ethnocentrism, is the consequence of a combination 

of cognitive and cultural biases resulting from a lifetime of enculturation, culturally 

bound heuristics, and missing, or inadequate, information. 3 

Ethnocentrism is a phenomenon that operates on a conscious level, but it is 

difficult to recognize in oneself and equally difficult to counteract. In part, this 

is because, in cases of ethnocentric thinking, an individual does not recognize 

that important information is missing or, more important, that his worldview 

and problem-solving heuristics interfere with the process of recognizing information 

that conflicts or refutes his assumptions. 

Take, for example, the proposition that others do not think like Americans. 

It seems only intuitive that other tribes, ethnic groups, nationalities, and states 

1 

Alexander Butterfield, The Accuracy of Intelligence Assessment; Richards J. Heuer, Jr., Psychology 

of Intelligence Analysis; Lisa Krizan, Intelligence Essentials for Everyone; J. R. Thompson, 

R. Hopf-Weichel, and R. Geiselman, The Cognitive Bases of Intelligence Analysis. 

2 

In this work, I use the broader term “ethnocentrism” to refer to the concept represented by mirror 

imaging and projective identification . 

3 

In anthropology, ethnocentrism is the tendency to judge the customs of other societies by the 

standards of one's own culture. This includes projecting one’s own cognition and norms onto others. 

75

CHAPTER SIX 

have different histories, languages, customs, educational practices, and cultures 

and, therefore, must think differently from one another. 

The problem, however, is that the cognitive process of understanding or 

even recognizing that there are cultural and cognitive differences is not intuitive 

at all. Intuition is the act of immediate cognition, that is, perceiving something 

directly through the use of culturally dependent heuristics and cognitive 

patterns accumulated through a lifetime without requiring the use of rational 

or formal processes. This effort appears doomed to failure, because “trying to 

think like them” all too often results in applying the logic of one’s own culture 

and experience to try to understand the actions of others, without knowing that 

one is using the logic of one’s own culture. This, however, does not have to be 

the case. Through acculturation and the use of specific strategies, tools, and 

techniques, it is possible to combat the effects of ethnocentrism without trying 

to “think like them.” This text includes two short case studies on failures to 

recognize ethnocentrism, both drawn from the author’s own experience and 

told from his perspective. These failures are then examined with the goal of 

developing strategies and techniques to combat ethnocentric bias. 

Case Study One: Tiananmen Square 

At the time of the prodemocracy protests of the Chinese students and, to a 

lesser extent, workers, between April and June of 1989, I too was a college 

student. I mention this because American college students and Chinese college 

students tend to perceive themselves in very different ways, and they are 

perceived by their societies as having very different social roles. Chinese students 

perceive themselves as having moral authority, and they are perceived 

as controlling social capital and possessing public status. There is a cultural 

norm in China that students, as the future elite, have a morally superior role in 

society. I remember thinking at the time that, with the obvious exception of 

those in power, who risked losing their privileged positions, any “rightminded” 

person in China would support democracy. A movement for democratic 

reform would liberalize the policies of a repressive regime, encourage 

personal freedom, and give the Chinese people a voice in their lives. 

When the university students went on strike and took over Tiananmen 

Square, the popular view in the United States, reflected in the US media, was 

that they were college students protesting for democratic reform. There were 

images of thousands of students rallying and camping out on and around the 

statue of the People’s Heroes. Throughout the square, banners and posters 

from universities supported democracy and freedom. The statue of the Goddess 

of Democracy erected by the demonstrators looked very much like our 

Statue of Liberty. Labor groups offered to join the students, people paraded in 

front of the Great Hall of the People, and citizens donated blankets and food. 

76

COMBATING ETHNOCENTRISM 

Student leaders began a hunger strike to force a dialogue between the students 

and the government. All signs seemed clearly to point to a popular movement 

for democracy, for which there was a groundswell of support. 

The Chinese government seemed hesitant or unsure. The People’s Liberation 

Army (PLA) was sent to surround the square, but citizens blocked their 

advance and tried to persuade the troops to be neutral. A curfew order was not 

obeyed; martial law was declared and ignored. Another PLA move on Tiananmen 

Square was repelled. It appeared that the students had forced a stalemate 

and that their demands would be heard. 

At that point, my assumption was that the government was weakened and 

would be forced to respond to the protesters’ demands, at least to some degree. 

I anticipated a dialogue and concessions on both sides. Although I imagined 

the government was capable of resorting to violence, I assumed that it would 

not. It seemed inconceivable that the citizens of Beijing—10–12 million people—would 

not intervene on behalf of the students. That many people could 

have overwhelmed the PLA had they chosen to do so. I also assumed that the 

soldiers of the PLA would be reluctant to fire on their own people, partly 

because the majority of both groups were from the same, dominant ethnic 

group of China, the Han, and, in part, because the soldiers represented a lower 

rung of Chinese society then did the students. The notion of soldiers killing 

students would be an affront to the sensibilities of the Han, or so I thought. I 

was wrong. 

In the end, when the PLA carried out its orders to clear the square with 

force and end the protest, support for the protesters turned out to be relatively 

slight. The Chinese “middle class” never came to the students’ aid; the great 

majority of the Beijing populace simply watched the events unfold. Moreover, 

it turned out that the labor groups participating in the demonstration were 

actually protesting against corporate corruption and the lack of job stability 

brought about by market reforms and not in support of the students’ demands 

for a loosening of restrictions on expression. What I perceived to be a groundswell 

of popular support for the students had been exaggerated and wishful 

thinking on my part. 

My failure to anticipate the way events would actual unfold in Tiananmen 

Square was tied to ethnocentric thinking and a lack of accurate and contextual 

information. Students in the United States are encouraged to be politically 

active, and their protests are often seen merely as minor inconveniences that 

need to be endured. In China, however, the protesting students were seen as a 

direct challenge to political authority and, much more so than in the United 

States, their actions were viewed as an outright conflict between the future 

elite and the current leadership. The protest itself was viewed as a violation of 

a taboo, upsetting the cultural order and the stability of society. 

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CHAPTER SIX 

As an observer, I missed the cultural context that was necessary to view the 

events as an actual conflict and could not convince myself that a violent solution 

was a possibility. I had discounted the hypothesis that violence would 

occur, because I could not imagine it occurring in the United States. This led 

me to discount raw data that would have refuted a hypothesis that the two factions 

would reach a compromise. In addition, at that time, I had no formal 

grounding in Chinese studies, nor had I been to China. Thus, I had not 

acquired information that would have helped me create a meaningful context 

for the event. 

Years later, my wife and I were in China doing ethnographic fieldwork on 

the socioeconomic effects of the spread of the English language and American 

culture in urban and rural China. 4 While there, we spent a great deal of time 

talking with others about the events of Tiananmen, and we decided to include 

in our research questions about the student protests, if for no other reason than 

to satisfy our own curiosity. 

What we found stood in contrast to media reports and the opinions 

expressed by many pundits and scholars in the US and the West. After hundreds 

of interviews with a wide variety of people in and around Beijing, we 

found a consistent preoccupation among the “silent majority.” That was the 

Cultural Revolution, which had affected all of the people we interviewed. 

They had been participants, observers, or survivors, and, often, all three. 

In the mid-1960s, Mao Zedong sought to recapture power from reformminded 

opponents within the Communist Party. Using radical party leaders as 

his instruments, he created the Red Guard, which was made up primarily of 

college students (although others followed suit in time). The image of the Cultural 

Revolution was not simply the image of Mao; it was also the image of 

angry, violent, and powerful college students, who were the most visible proponents 

of the “Cult of Mao.” According to the people we interviewed, it was 

the students who had chanted slogans, raised banners, paraded in public 

spaces, resisted older forms of social control, and seized power. With that 

power and the blessings of Mao, the youth and university students had committed 

many of the atrocities of the Cultural Revolution and plunged China 

into a decade of chaos, during which many institutions, including schools, 

were closed and many of the country’s cultural and historical artifacts were 

destroyed. 

4 

American anthropology is based on the ethnographic method and direct interaction with the people 

who are being studied. This interaction includes direct and participant observation and interviews, 

or fieldwork, where one lives with the people being investigated. Continental European 

schools of anthropology are not as obsessed with methodology and hands-on experience and tend 

to the more theoretical. 

78


At the height of the Cultural Revolution, any dissent was sufficient to bring 

accusations of counterrevolutionary sympathies and to qualify one for “reeducation,” 

which could mean public denunciation, job loss, incarceration, 

forced labor, relocation, and even murder, torture, and rape. The traditional 

values of respect and honor were replaced with violence and terror, and the 

historical social unit of the family had been disrupted and replaced with the 

cult of Mao. 

For those who had lived through the Cultural Revolution, the student challenge 

to the government in Tiananmen in 1989 was also a challenge to social 

order and stability. The people we interviewed remembered, correctly or not, 

that the faction of the Communist Party then in power and the PLA had 

stopped the Red Guard and the Cultural Revolution, arrested its highest ranking 

proponents and beneficiaries, the Gang of Four, and eventually restored 

order to the nation. The point of view of the people we interviewed was that 

the PLA, despite the low social status of soldiers, had stopped the chaos. 

Although they did not approve of killing students, the threat of another cultural 

revolution, democratic or otherwise, was more disturbing to them than 

the bloody climax in the square. Social order was the higher virtue. 

Tiananmen Square: Discussion 

We cut nature up, organize it into concepts, and ascribe significances 

as we do, largely because we are parties to an agreement to 

organize it in this way—an agreement that holds throughout our 

speech community and is codified in the patterns of our language. 

Benjamin Whorf 5 

In 1987, a Chinese academic, Min Qi, performed the first national survey of 

Chinese political culture. 6 Respondents were asked, among other things, to 

select statements that best described their understanding of democracy. Of the 

1,373 respondents, 6.6 percent responded that democracy meant that people 

could elect their political leaders and 3.4 percent that power was limited and 

divided. These replies tended to be from individuals under 25 years of age, in 

college, and living in urban centers. 

5 

Benjamin Whorf, along with fellow anthropologist Edward Sapir, developed the linguistic relativity 

hypothesis, asserting that different speech communities had different patterns of thought. 

Although challenged by linguist/philosopher Noam Chomsky and others with the Universal 

Grammar hypothesis, linguistic relativity still has a significant amount of empirical research support. 

Benjamin Whorf, Language, Thought, and Reality. 

6 

Min Qi, Zhongguo Zhengzhi Wenhua [Chinese Political Culture]. Translation courtesy of a 

friend of the author who prefers to remain anonymous. 

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CHAPTER SIX 

In contrast, 25 percent responded that democracy was guided by the center 

(the party and the cadres), 19.5 percent that democracy meant that the government 

would solicit people’s opinions (the party would ask people what they 

thought), and 11 percent that democracy meant the government would make 

decisions for the people based on the people’s interests but not including the 

people’s direct vote. These three responses were more in line with then-current 

party doctrine and tended to be from individuals over 36 years of age living in 

both urban and rural settings. This was the same demographic that experienced 

the Cultural Revolution. 

The election of representatives and the division and limitation of those representatives’ 

power—what I would have considered to be two key aspects of 

democracy—were chosen by 10 percent of the sample, only slightly larger 

than the 6.3 percent of Chinese respondents who reported that they didn’t 

know what the word “democracy” meant. My own perception of democracy 

fit with a young, urban, elite, college educated population, not with the majority 

of Chinese citizens. 

There was a very small sample of citizens in Tiananmen Square demanding 

what looked and sounded like my American version of democracy. Yet, however 

much the students’ message resonated in the West, it did not do so in 

China. My expectations notwithstanding, there was a cognitive disconnect 

between students and average citizens, which, along with the visceral semiotics 

of the Cultural Revolution, kept the two apart. 7 It was not just the message 

that had kept people in their homes during the PLA siege on Tiananmen; it 

was also the messengers. 

The label “ethnocentrism” might be accurate, but it does not diagnose the 

root of the problem. I did not use a variety of tools or techniques to question 

my underlying assumptions and, therefore, I failed to make an accurate forecast. 

There were obvious statistical and analytic flaws. The former was principally 

a sampling error, both frame and selection bias (the students at 

Tiananmen did not represent the general population in Beijing or China at 

large). More significant than simple technical or statistical flaws, however, my 

frame of reference and my assumptions about meanings, context, and values 

(or culture) misled me. 

The assumptions I made about the Tiananmen protests were products of my 

own enculturation, and I am not convinced that anything short of the experience 

of analytic failure would have been sufficient for me to examine the process 

underpinning my reasoning. I never would have reexamined my mental 

mode without experiencing failure. Failure is an event that is easily remem- 

7 

Semiosis is the production of cultural signifiers or signs and the cultural or contextual meaning 

of those signs. This includes all modes of visual and auditory production. 

80


bered; it affects the ego and drives one to investigate errors and to adapt or 

change behavior based on those investigations. Failure is a learning event and 

results in a teachable moment. 8 

There seems to be little reason to perform a postmortem when events unfold 

as predicted. The natural assumption is that the mechanisms of analysis were 

valid, because the results of the analysis were accurate. The obvious danger is 

that this assumption discounts the possibility that one may be accurate purely 

by accident. Moreover, by focusing only on failure, one risks sampling bias by 

only choosing cases in which there was error. The risk of ignoring success is 

that potential lessons may go undiscovered. An alternative to relying on failure 

to challenge one’s assumptions is to create a standard practice of reviewing 

each case regardless of outcome, principally through the use of a formal 

After Action Review (AAR). 

Case Study Two: The Red Team 

Recently, I was asked to serve on a newly formed red team within the 

Department of Defense. I agreed to participate, despite a number of serious 

concerns having to do both with the nature and structure of red teams in general 

and with my own experience with ethnocentrism and its effects on analysis. 

These concerns are applicable not only to red teams, but also to any 

analyst put in the position of trying to “think like them.” 9 

This particular red team was part of a constructive/conceptual war game in 

which there were 11 participants, seven of whom had doctorates. Of the seven 

doctorates, three were psychologists, one was a historian, one was an economist, 

one was a political scientist, and one was an anthropologist. The other 

four participants had extensive military backgrounds. There were no physical 

scientists or engineers. Nine of the 11 participants were white males, one was 

a male born in the region of interest, and one was a white female. All were 

middle class. Seven of the 11 were raised in nominally Christian homes and 

three in nominally Jewish homes. (I say nominally because it was not possible 

to determine their level of religious commitment during this exercise.) 

I mention the demographics of the group because it was not representative 

of the adversary we were intended to simulate. Although the group had 

8 

Charles Perrow, Normal Accidents. 

9 

Military red teams are meant to simulate the actions of an adversary in some type of war game or 

crisis simulation, usually with the goal of generating scenarios for training and readiness or for 

logistics and planning. These war games may be live, e.g., force-on-force simulations like those of 

the US Army Combat Training Centers; virtual, as in flight simulators; or constructive, either digital 

theater-level simulations or purely conceptual games centered on strategic, tactical, or operational 

issues. 

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CHAPTER SIX 

numerous domain matter experts, very few had first-hand knowledge of the 

region of interest. Only one participant was from the area, had spent formative 

years there, spoke the languages, and experienced the culture firsthand. As 

this group was assembled to simulate the behavior and decisionmaking of a 

foreign adversary, this aspect was more important than it would have been for 

a substantive team developing threat assessments around a specific topic or 

target. Consequently, the scenarios developed by the red team often reflected 

an adversary whose behavior and decisionmaking resembled those of educated, 

white, middle class Americans. 

The one member of the red team who had been born in and spent formative 

years in the region of interest regularly stopped the scenario development process 

by saying, “They wouldn’t do that” or “They don’t think that way.” On 

several occasions, he objected, “This scenario is way too complex” or “They 

wouldn’t use that tactic; it requires too much direct communication.” His 

objections were not usually based on military considerations; rather, they were 

based on the cultural norms and mores of the adversary. He talked of kinship 

relationships as a specific type of social network in the region and of the value 

of kinship for understanding the adversary’s intentions. In short, he brought an 

ethnographic perspective to the exercise. 

Having no personal or professional experience with this region or its cultures, 

I thought it appropriate to defer to his first-person experience. Ultimately, 

however, it proved difficult to convince the group that this man’s 

cultural knowledge was, in fact, an area of specialized knowledge that needed 

to be factored into each scenario. This difficulty was born out of another type 

of ethnocentric bias. 

Inviting an anthropologist to a red team exercise presupposes that the red 

team takes seriously the notion that cultural differences matter and that those 

cultural factors ought to be made explicit in the analytic process. The problem 

in this case was that the anthropologist was not an area expert for this region 

and its cultures, and the one area expert who was there lacked the academic 

credentials to be taken seriously by the other members of the group. Had I 

been able to assert the same concepts that the other individual asserted, it 

would have had a certain academic, or scientific, imprimatur because of my 

training and experience. Because he lacked these credentials, many of the 

other individual’s insights were lost, and the analytic product suffered as a 

result. 

The Red Team: Discussion 

I am reluctant to fault the organizers for the ethnocentric bias in the demographic 

composition of the red team. It is very difficult to assemble a truly 

82


representative red team. There is the obvious problem of security. Someone 

fully able to represent the adversary culturally would very likely be unable to 

obtain requisite clearances for participation in a classified red team exercise. 

In fact, even if it were possible to find someone both culturally representative 

and sympathetic to the goals of the red team, such as the participant born in 

the region, the conflicts triggered by that sympathy, cultural identity, and cultural 

allegiance could well lead to unforeseen cognitive biases that would be 

difficult to counteract. 10 

An alternative is to find an ethnic American citizen with similarities to the 

people of the region of interest, but simply finding a US citizen with the same 

ethnicity as those of the region of interest does not guarantee any special 

insight into their thinking. Ethnicity is not the same as sharing culture or identity. 

Not all ethnic groups in the US are isolated and self-perpetuating. Many, 

in fact, put great effort into trying to assimilate into the larger “American culture” 

by distancing themselves from their culture of origin. These people often 

struggle with their own concept of cultural identity and the broader issues of 

community affiliation. 11 Many immigrants and most first-generation offspring 

have already begun the process of acculturation. More striking, their offspring 

display a process of enculturation in the US by learning the language, attending 

the schools, assimilating local and national values, and establishing ties to 

a diverse community outside of their own ethnic enclave. In fact, the children 

of recent immigrants share many of the same cognitive filters as those who are 

generations removed from migration. That said, there are American citizens 

born in the region of interest, like the member of the red team in which I participated, 

who do have insight into specific cultures, principally because their 

enculturation was affected by being born in, and living in, a foreign region. 

The participant in that red team was a foreign-born American citizen, but 

foreign birth is not a necessary condition for enculturation. 12 Living in a foreign 

region, speaking the language, interacting with the people, developing 

community ties, and establishing an identity within that community are all 

part of the acculturation process and allow one to alter the cognitive filters 

through which one interprets the world. Time spent on a US military base, in a 

US embassy, or in a Western hotel overseas does not lead to acculturation. 

10 

Philip Cushman, Constructing the Self, Constructing America; John Lucy, Language Diversity 

and Thought; Douglass Price-Williams, Explorations in Cross-Cultural Psychology; Marshall 

Segall, Cross-Cultural Psychology; Richard Shweder, Thinking Through Cultures; Yali Zou and 

Enrique Trueba, Ethnic Identity and Power; and Benjamin Whorf. 

11 

David Levinson and Melvin Ember, American Immigrant Cultures. For raw data covering 186 

cultural groups since 1937, including immigrants, see the Human Relations Area Files at Yale 

University. 

12 

Some anthropologists have argued that enculturation is specific to childhood but the evidence 

supports that it is a lifelong process. See Segall. 

83

CHAPTER SIX 

Quite the contrary, each of these is a “virtual” America, an approximation of 

life in the United States on some foreign soil, and it is the time spent away 

from these institutions that is important. 

The red team experience reinforced lessons I learned from my own analytic 

failures and biases. Watching the struggle between the man enculturated in the 

region of interest and the academic experts was a frustrating experience. It 

was clear that the experts would not, or could not, hear what he was saying 

and that neither he nor I knew how to get the other experts to listen. I doubt 

this communication failure was the result of stubbornness or arrogance on 

anyone’s part. It seemed rather that the experts’ thinking naturally defaulted to 

their own cultural reference points, which interfered with his attempts to communicate 

his cultural knowledge. 

Specific cultural knowledge is a skill and the foundation for forecasting the 

behavior and decisionmaking of foreign actors. Acquiring cultural knowledge 

should be taken as seriously as learning any other facet of one’s analytic capabilities. 

Moreover, it is incumbent on analysts to educate their own leadership 

and policymakers about the value and utility of cultural knowledge for intelligence 

analysis. 

Conclusion and Recommendations 

Ethnocentrism is a normal condition, and it results in analytic bias. The analytic 

community and intelligence researchers need to develop tools and techniques 

to combat analytic ethnocentrism. I believe that using cultural diversity 

as a strategy to combat ethnocentrism has much to recommend it. 13 

Security concerns may make it very difficult, if not impossible, to hire people 

who are genuinely representative of a given culture. As an alternative to 

focusing on hiring practices, I recommend a formal cultural training program 

to facilitate acculturation. The program would include language acquisition 

and a classroom segment centered on specific cultures, but it would go beyond 

these by having the students go to countries of interest and interact with the 

13 

Some social action groups have appropriated the words “cultural diversity” from Levi-Strauss 

and the French school of structural anthropology as a rallying cry to advance an agenda of equal 

access to resources and power. That is, the concept has been politicized, and, invoking the words 

“cultural diversity” in a public forum ensures that people will have some emotional reaction. This 

is not my intention. The use of the words in this work is meant strictly in the technical sense, specifically, 

that is, to refer to individuals whose enculturation occurs among different cultures or 

individuals who have experienced acculturation. Acculturation is not specific to any one group, 

all people can and do experience acculturation to one degree or another through cultural contact 

and cultural diffusion, defined as the spreading of a cultural trait (e.g., material object, idea, or 

behavior pattern) from one society to another without wholesale dislocation or migration. Moreover, 

acculturation can be accomplished purposefully through training and fieldwork. 

84


people in their own setting and on their own terms. Students would be encouraged 

to investigate the rituals, norms, taboos, kinship systems, and social networks 

of the cultures being studied. There would also be provision for 

continuing on-line education and an on-line community of practice for mentoring, 

problem solving, and peer-to-peer interaction. 

In my view, a stand-alone training program would be insufficient to affect 

analytic processes without specific follow-on programs. Retention of training 

requires repetition, problem solving, application, and evaluation. People must 

use what they learn and then determine if what they have learned can improve 

the quality of their work. To this end, I recommend a formal After Action 

Review (AAR) process. 

The AAR is used by the US Army to capture lessons learned after a training 

exercise or a live operation. Unlike conventional postmortems and traditional 

performance critiques, the AAR is used to evaluate successes as well as failures. 

Although failure generally receives more scrutiny and attention than success, 

an approach that only examines failure results in sampling error. If one 

only scrutinizes mistakes, otherwise effective methods may be blamed for the 

errors. That those techniques were successful in 99 out of 100 cases can go 

unnoticed, with the result that the failures receive disproportionate attention 

and bias the statistical results of the postmortem. The AAR was specifically 

designed to avoid this problem. 

The AAR process was introduced in the mid 1970s, but it is based on the 

oral history method of “after combat interviews” employed by S.L.A. Marshall 

during World War II, the Korean War, and the Vietnam War. As soon as 

possible after a battle, regardless of the outcome, Marshall would assemble 

soldiers who were involved and, using a semistructured interview technique, 

would engage them in a group discussion about their individual and team roles 

and actions during combat. 

The current AAR method also includes such objective data as tactics, logistics, 

kill ratios, time-to-task, accuracy-of-task, and operational outcomes. 14 

Informed by the objective data, a group discussion led by a facilitator trained 

in the elicitation process ensues. The AAR, along with supporting documents, 

such as historical studies and relevant doctrinal materials, is then stored in a 

knowledge repository at the US Army’s Center for Army Lessons Learned 

(CALL). 15 

With some customization, an AAR process and a lessons learned repository 

could be created for intelligence analysts. Although seemingly time-consuming 

and cumbersome, with training and expert facilitators, the AAR process could be 

modified and streamlined for use by analysts at the end of a production cycle. As 

14 

John Morrison and Larry Meliza, Foundations of the After Action Review Process. 

85

CHAPTER SIX 

a practical matter, the process would be used mostly with longer works, such as 

assessments or estimates. The intelligence product, along with AAR notes, would 

then be incorporated in a community knowledge repository. This knowledge 

repository would also help in the development and refinement of advanced analytic 

courses by providing course developers with baseline analytic data. In short, 

the repository becomes a tool for continuous educational needs analysis and links 

training directly to the actual work practices of analysts. These data can be used 

as a test bed for research on the effectiveness of analytic methodology. In this 

way, the lessons learned are not lost to future generations of analysts. 

15 

See the US Army Center for Army Lessons Learned Web site, which has links to numerous 

other repositories. Although each organization has customized the concept to meet its unique 

needs, all of the US military services, the National Aeronautics and Space Administration, the 

Department of Energy, the Environmental Protection Agency, the North Atlantic Treaty Organization, 

the United Nations, and the ministries of defense of Australia and Canada, currently have 

Lessons Learned repositories. 

86

CHAPTER SEVEN 

Instructional Technology: Effectiveness and 

Implications for the Intelligence Community 

J. D. Fletcher 1 

Rob Johnston 

The Intelligence Community has begun to invest substantial resources in 

the training and education of its analysts. With the exception of a few 

advanced courses available through distance learning networks, this instruction 

is delivered using a conventional classroom model. This model possesses 

a number of inherent inefficiencies, including inconsistent instruction, strict 

ties to time and place of instruction, large student-to-instructor ratios, and limited 

active participation by students due to class size and scheduling. 

Research suggests that significant improvements can be achieved through 

the use of computer-based instructional technology. According to these studies, 

this technology can increase instructional effectiveness and reduce time 

needed to learn. It can achieve these efficiencies, moreover, while both lowering 

the cost of instruction and increasing its availability. 2 This chapter summarizes 

evidence on the promise of instructional technology for intelligence 

analysis training. 

1 

Dr. J. D. Fletcher is a research staff member at the Institute for Defense Analyses, where he specializes 

in issues of manpower, personnel, and training. He holds graduate degrees in computer 

science and educational psychology from Stanford University. 

2 

Because instructional technology makes few distinctions between formal education and professional 

training, the term “instruction” will be used for both in this chapter. 

87

CHAPTER SEVEN 

Background 

The argument for the use of instructional technology usually begins with a 

comparative examination of the effectiveness of classroom instruction and 

individual tutoring. For instance, the graph below illustrates the combined findings 

of three dissertation studies that compared one-on-one tutoring with oneon-many 

classroom instruction. 3 

It is not surprising that such comparisons would show that tutored students 

learned more than those taught in classrooms. What is surprising is the magnitude 

of the difference. Overall, as the figure shows, it was two standard devia- 

Individual Tutoring Compared to Classroom Instruction 

tions. This finding means, for example, that with instructional time held fairly 

constant one-on-one tutoring raised the performance of 50th percentile students 

to that of 98th percentile students. These, and similar empirical research findings, 

suggest that differences between one-on-one tutoring and typical classroom 

instruction are not only likely, but also very large. 

Why then do we not provide these benefits to all students? The answer is 

straightforward and obvious. With the exception of a few critical skills, such as 

aircraft piloting and surgery, we cannot afford it. One-on-one tutoring has been 

described as an educational imperative and an economic impossibility. 4 

3 

Benjamin S. Bloom, “The 2 Sigma Problem: The Search for Methods of Group Instruction as 

Effective as One-to-One Tutoring.” The dissertation studies were performed under Bloom’s direction. 

4 

M. Scriven, “Problems and Prospects for Individualization.” 

88

INSTRUCTIONAL TECHNOLOGY 

The success of one-on-one tutoring may be explained by two factors. First, 

measured in terms of questions asked and answered, tutors and their students 

engage in many more instructional interactions per unit of time than is possible 

in a classroom. Second, one-on-one tutoring can overcome the substantial 

spread of ability, measured by the time needed to reach minimal proficiency, 

that is found in practically every classroom. Tutoring reduces time-to-learn by 

adapting each interaction to the needs of each student. Less time is spent on 

material the student has already learned, and more time is spent on material 

remaining to be mastered. 

To investigate the intensity of instructional interactions, Art Graesser and 

Natalie Person compared questioning and answering in classrooms with those 

in tutorial settings. 5 They found that classroom groups of students ask about 

three questions an hour and that any single student in a classroom asks about 

0.11 questions per hour. In contrast, they found that students in individual tutorial 

sessions asked 20–30 questions an hour and were required to answer 117– 

146 questions per hour. Reviews of the intensity of interaction that occurs in 

technology-based instruction have found even more active student response 

levels. 6 

Differences in the time needed by individuals in any classroom to meet 

instructional objectives are also substantial. Studies on this issue have reported 

ratios varying from 1:3 to 1:7 in the times the fastest learners need to learn compared 

to the times needed by the slowest learners. Although these differences 

may be due initially to ability, these studies suggest that such ability is quickly 

overtaken by prior knowledge of the subject matter. 7 This effect is particularly 

evident in instruction for post-secondary-school students, because prior knowledge 

rapidly increases with age and experience. Technology-based instruction 

has long been recognized for its ability to adjust the pace of instruction to individual 

needs, advancing through instructional material as quickly or as slowly as 

required. The overall result has been substantial savings in the time required to 

meet given instructional objectives. 8 

It should be emphasized that these benefits are not achieved at the expense 

of instructional quality. Research has found that many instructional technologies 

have a positive impact on learning across a wide variety of student populations, 

settings, and instructional subject matters. 9 

This research suggests that technology-based instruction results in substantial 

savings of time and money. Studies have shown that the times saved aver- 

5 

Art Graesser and Natalie Person, “Question-Asking During Tutoring.” 

6 

J. D. Fletcher, Technology, the Columbus Effect, and the Third Revolution in Learning. 

7 

Sigmund Tobias, “When Do Instructional Methods Make a Difference?” 

8 

J. D. Fletcher, “Evidence for Learning From Technology-Assisted Instruction.” 

9 

Ken Spencer, “Modes, Media and Methods: The Search for Educational Effectiveness.” 

89

CHAPTER SEVEN 

age about 30 percent, as seen in the table below. The reduction in overhead 

expenses averages 20–30 percent. 10 Research has shown that the cost ratios 

(calculated as the ratio of experimental intervention costs over the costs of a 

control group) for interactive multimedia technology (computer-based instruction 

with enhanced audio, graphics, and/or video; CD-ROM and DVD-based 

instruction; interactive video, etc.) favor it over conventional instruction along 

with time savings of about 31 percent. 11 Simulation of such systems as helicopters, 

tanks, and command-control systems for training combat skills has 

also proven to be cost-effective. 12 The operational costs for simulation are, on 

average, 10 percent of the costs of using the actual systems to train. 13 

Time Savings for Technology-Based Instruction 

Study (Reference) Number of Studies 

Reviewed 

Average Time Saved 

(Percent) 

Military Training - 

13 54 

Orlansky 

Higher Education - 

8 31 

Fletcher 

Higher Education - 

17 34 

Kulik 

Adult Education - Kulik 15 24 

Meta-analysis Demonstrates the Effectiveness of Instructional 

Technology 

Researchers often use a meta-analytic approach to review and synthesize 

quantitative research studies on a variety of issues, including instructional 

effectiveness. 14 This method involves a three-step process, which begins with 

the collection of studies relevant to the issue using clearly defined procedures 

that can be replicated. Next, a quantitative measure, “effect size,” is used to 

tabulate the outcomes of all the collected studies, including those with results 

that are not statistically significant. Finally, statistical procedures are used to 

10 

Jesse Orlansky and Joseph String, Cost-Effectiveness of Computer-Based Instruction in Military 

Training; H. Solomon, Economic Issues in Cost-Effectiveness Analyses of Military Skill 

Training; James Kulik, “Meta-Analytic Studies of Findings on Computer-Based Instruction”; Rob 

Johnston, “The Effectiveness of Instructional Technology”; Ruth Phelps et al., “Effectiveness and 

Costs of Distance Education Using Computer-Mediated Communication”; J. D. Fletcher Effectiveness 

and Cost of Interactive Videodisc Instruction in Defense Training and Education. 

11 

J. D. Fletcher, “Computer-Based Instruction: Costs and Effectiveness.” 

12 

Jesse Orlanksy et al., The Cost and Effectiveness of the Multi-Service Distributed Training Testbed 

(MDT2) for Training Close Air Support. 

13 

Jesse Orlansky et al., The Value of Simulation for Training. 

14 

Gene Glass, “Primary, Secondary, and Meta-Analysis of Research.” 

90


synthesize the quantitative measures and describe the findings of the analysis. 

Meta-analysis appears to be especially suited for synthesizing the results of 

instructional research, and it has been widely used for this purpose since its 

introduction in 1976. 

Meta-analysis is still being developed as a technique, and some matters 

concerning its use, notably the “file-drawer” problem and calculation of effect 

size, remain unsettled. Chapter Twelve presents a more detailed explanation 

and these considerations. Briefly, however, meta-analytic reviews of instructional 

technology effectiveness have found substantial results favoring its use 

over traditional technologies of classroom instruction. 

Overall, effect sizes for post-secondary school instruction average about 

0.42, which is roughly equivalent to raising the achievement of 50th percentile 

students to that of 66th percentile students. 15 Reviews of more elaborate forms 

of instructional technology, such as those using applied artificial intelligent 

techniques, have found effect sizes in excess of 1.0, which is roughly equivalent 

to raising the achievement of 50th percentile students to that of the 84th 

percentile. 16 It seems reasonable to conclude that the reduced costs and 

reduced time to learn obtained in applications of instructional technology are 

not achieved at the expense of instructional effectiveness. 

Encouraging as these favorable results are, our ability to apply instructional 

technology efficiently may be in its infancy. Findings thus far have been based 

on instructional applications intended to teach facts (e.g., What is the capital 

of Brazil? What is the Spanish word for chapel? Who was the first director of 

the Central Intelligence Agency?) concepts (e.g., What is a mass spectrometer 

used for? What is the difference between micro- and macro-economics? When 

must you use a torque wrench?), and procedures (e.g., How do you record a 

movie from television? How do you prepare a purchase requisition? How do 

you calibrate a radar repeater?). All intelligence analysts must possess a repertoire 

of facts, concepts, and procedures to perform their craft, and instructional 

technology holds great promise for increasing both the efficiency with which 

they might develop this repertoire and their access to instructional resources 

for doing so. 

However, the capabilities analysts may seek through instruction are likely 

to include more abstract, or “higher,” cognitive processes. For instance, in 

addition to learning a procedure, analysts may need the capability to recognize 

15 

Chen-Lin Kulik., James Kulik and Barbara Shwalb, “Effectiveness of Computer-Based Adult 

Education: A Meta-Analysis”; Chen-Lin Kulik and James Kulik, “Effectiveness of Computer- 

Based Education in Colleges”; Rob Johnston and J. D. Fletcher, A Meta-Analysis of the Effectiveness 

of Computer-Based Training for Military Instruction; J. D. Fletcher, “Evidence for Learning 

from Technology-Assisted Instruction.” 

16 

Sherrie P. Gott, R. S. Kane, and Alan Lesgold , Tutoring for Transfer of Technical Competence. 

91

CHAPTER SEVEN 

the procedure’s applicability in unfamiliar situations, modify it as needed, and 

use it to develop new approaches and procedures. Early on, Bloom discussed 

learning objectives as a hierarchy beginning with knowledge at the most rudimentary 

level and ascending through comprehension, application, analysis, 

and synthesis to evaluation. 17 Bloom’s is not the only such hierarchy to 

emerge from research on instructional design, but it seems to be the best 

known, and it describes as well as any the various levels of knowledge, skill, 

and ability to which learners may aspire. 

Current Research on Higher Cognitive Abilities 

Analysts have begun to discuss development of the higher cognitive abilities 

needed to deal with unanticipated and novel challenges. 18 Components of 

such “cognitive readiness” may include: 

• Situation awareness—the ability to comprehend the relevant aspects of a 

situation and use this understanding to choose reasonable courses of 

action. 19 Practice and feedback in complex, simulated environments have 

been shown to improve situation awareness. 

• Memory—the ability to recall and/or recognize patterns in a situation that 

lead to likely solutions. It may be supported by two underlying theoretical 

mechanisms: encoding specificity, 20 which stresses the importance of 

responding to relevant external and internal perceptual cues, and transferappropriate 

processing, 21 which stresses the actions performed during 

encoding and retrieval. Some instructional techniques, such as overlearning, 

22 have been shown to enhance long-term retention. 23 

• Transfer—the ability to apply what is learned in one context to a different 

context. It can be perceived either as the ability to select and apply procedural 

knowledge gained in one context to another (“low road” transfer) or 

as the ability to apply the principles abstracted from a set of contexts to 

another (“high road” transfer). 24 Extensive practice, with feedback, will 

17 

Benjamin. S. Bloom, Taxonomy of Educational Objectives. 

18 

J. E. Morrison, and J. D. Fletcher, Cognitive Readiness. 

19 

M. R. Endsley, “Design and Evaluation for Situation Awareness Enhancement.” 

20 

E. Tulving and D. M. Thomson, “Encoding Specificity and Retrieval Processes in Episodic 

Memory.” 

21 

C. D. Morris, J. D. Bransford, and J. J. Franks, “Level of Processing Versus Transfer-Appropriate 

Processing.” 

22 

The use of specific problem-solving methods repetitively. 

23 

R. A. Wisher, M. A. Sabol, and J. A. Ellis Staying Sharp: Retention of Military Knowledge and 

Skills. 

92


enhance the former. Instruction in developing mental abstractions will 

enhance the latter. 

• Metacognition—the executive functions of thought, more specifically, 

those needed to monitor, assess, and regulate one’s own cognitive processes. 

25 Meta-cognitive skills can be enhanced by exercises designed to 

increase awareness of self-regulatory processes. 26 

• Pattern Recognition—the ability to distinguish the familiar from the unfamiliar. 

It may be accomplished by “template matching,” which involves 

comparing retained images with incoming sensory impressions; or by 

“feature comparison,” which involves recognizing and generalizing from 

distinctive features of a structure held in memory with incoming sensory 

impressions. 27 Pattern recognition can be taught through a combination of 

extensive practice, with feedback, and instruction in forming abstractions. 

• Automaticity—processes that require only limited conscious attention. 28 

Automaticity can be taught by providing extensive practice, with feedback. 

• Problem Solving—the ability to analyze a situation and identify a goal or 

goals that flow from it, identify tasks and subtasks leading to the goal, 

develop a plan to achieve them, and apply the resources needed to carry out 

the plan. Practice, with feedback, and overlearning can enhance problemsolving 

ability in many tasks. Techniques for problem solving can be successfully 

taught, as can the knowledge base needed to implement them. 29 

• Decisionmaking—a component of problem solving, but the emphasis in 

decisionmaking is on recognizing learned patterns, reviewing courses of 

action, assessing their impact, selecting one, and allocating resources to 

it. 30 Instruction in assessing courses of action has been shown to improve 

decisionmaking, but some aspects of successful decisionmaking are more 

likely to be inborn than trained. 

• Mental Flexibility and Creativity—the ability to generate and modify 

courses of action rapidly in response to changing circumstances. 31 It 

24 

G. Salomon and D. N. Perkins “Rocky Roads to Transfer: Rethinking Mechanisms of a 

Neglected Phenomenon.” 

25 

J. H. Flavell, “Metacognitive Aspects of Problem Solving.” 

26 

D. J. Hacker, Metacognition: Definitions and Empirical Foundations [On-line Report]. 

27 

M. H. Ashcraft, Fundamentals of Cognition. 

28 

R. M. Shiffrin and W. Schneider, W. “Controlled and Automatic Human Information Processing: 

II. Perceptual Learning.” 

29 

J. R. Hayes, The Complete Problem Solver. 

30 

P. Slovic, S. Lichtenstein, and B. Fischoff, “Decision-making.” 

31 

D. Klahr, & H. A. Simon, “What Have Psychologists (and Others) Discovered About the Process 

of Scientific Discovery?” 

93

CHAPTER SEVEN 

includes the ability to devise plans and actions that differ from and 

improve upon “school solutions.” Capabilities that widen the range of 

options can be taught, but higher levels of creativity are more likely to be 

inborn than trained. 

The above review suggests, first, that the creative processes needed by analysts 

can, to some extent, be broken down into components, and second, that 

these components can, again to some extent, be taught. Instructional technology 

can now substantially aid analysts in acquiring the facts, concepts, and 

procedures needed to perform their craft. However, it must become increasingly 

“intelligent” if it is to compress the years of experience analysts now 

need to become proficient and help them more rapidly acquire the advanced 

cognitive capabilities—those higher in Bloom’s hierarchy—that they also 

need. To do this successfully, instruction must be tailored to the specific background, 

abilities, goals, and interests of the individual student or user. Instructional 

technology must provide what has been called “articulate expertise.” 

Not only must it supply helpful and relevant guidance in these more advanced 

levels of knowledge, skills, and abilities, it must do so in a way that learners 

and users with varying levels of knowledge and skill can understand. 

Discussion 

At this point, it may be worth reviewing the capabilities provided by “nonintelligent” 

instructional technology since the 1950s. It has been able to: 32 

• accommodate the rate of progress of individual students, allowing as 

much or as little time as each needs to reach instructional objectives; 

• tailor both the content and the sequence of instructional content to each 

student’s needs; 33 

• make the instruction easy or difficult, specific or abstract, applied or theoretical 

as necessary; 

• adjust to students’ most efficient learning styles (collaborative or individual, 

verbal or visual, etc.). 

Intelligent tutoring systems are a different matter. They require quite specific 

capabilities that were first targeted in the 1960s. 34 Two key capabilities 

are that intelligent tutoring systems must: 

32 

E. Galanter, Automatic Teaching; R. C. Atkinson and H. A. Wilson, Computer-Assisted 

Instruction; P. Suppes and M. Morningstar, Computer-assisted Instruction at Stanford 1966-68; J. 

D. Fletcher and M. R. Rockway, “Computer-based Training in the Military.” 

33 

J. S. Brown, R. R. Burton, and J. DeKleer, “Pedagogical, Natural Language and Knowledge 

Engineering in SOPHIE I, II, and III.” 

94


• allow either the system or the student to ask open-ended questions and initiate 

instructional, “mixed-initiative” dialogue as needed or desired; 

• generate instructional material and interactions on demand instead of 

requiring developers to foresee and store all the materials and interactions 

needed to meet all possible eventualities. 

Mixed-initiative dialogue requires a language for information retrieval, 

tools to assist decisionmaking, and instruction that is shared by both the system 

and the student/user. The system must have the capability (referred to as 

“generative capability”) to devise, on demand, interactions with students that 

do not rely on predicted and prestored formats. This capability involves more 

than generating problems tailored to each student’s needs. It must also provide 

the interactions and presentations that simulate one-on-one tutorial instruction, 

including coaching, hints, and critiques of completed solutions. 

Cost containment is one motivation for wanting to generate responses to all 

possible student states and actions instead of attempting to anticipate and store 

them. Another arises from basic research on human learning, memory, perception, 

and cognition. As documented by Neisser among others, during the 

1960s and 1970s, the emphasis in basic research on human behavior and on 

the way in which it is understood shifted from the strict logical positivism of 

behavioral psychology, which focused on directly observable actions, to consideration 

of the internal, cognitive processes that were needed to explain 

empirically observed behavioral phenomena and are assumed to mediate and 

enable human learning. 35 

The hallmark of this approach is the view that seeing, hearing, and remembering 

are all acts of construction, making more or less use of the limited 

stimulus information provided by our perceptual capabilities. Constructivist 

approaches are the subject of much current and relevant discussion in instructional 

research circles, but they are firmly grounded in the foundations of scientific 

psychology. 36 For instance, in 1890, William James stated his General 

Law of Perception: “Whilst part of what we perceive comes through our 

senses from the object before us, another part (and it may be the larger part) 

always comes out of our mind.” 37 

In this sense, the generative capability sought by intelligent instructional 

systems is not merely something nice to have. It is essential if we are to 

34 

J. R. Carbonell, “AI in CAI: An Artificial Intelligence Approach to Computer-Assisted Instruction”; 

J. D. Fletcher & M. R. Rockway. 

35 

U. Neisser, Cognitive Psychology. 

36 

For example, T. M. Duffy, and D. H. Jonassen, Constructivism and the Technology of Instruction; 

S. Tobias and L. T. Frase, “Educational psychology and training.” 

37 

William James, Principles of Psychology: Volume I. 

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CHAPTER SEVEN 

advance beyond the constraints of the prescribed, prebranched, programmed 

learning and ad hoc principles commonly used to design technology-based 

instruction. The long-term vision is that training, education, and performance 

improvement will take the form of human-computer conversations. 

There has been progress toward this end. This conversational capability has 

been realized in systems that can discuss issues with students using a formal 

language, such as computer programming or propositional calculus. 38 More 

recent research suggests that significantly improved natural-language dialogue 

capabilities can be achieved by instructional technology. 39 Such an 

interactive, generative capability is needed if we are to deal successfully with 

the extent, variety, and mutability of human cognition. Much can now be 

accomplished by instructional technology, but much more can be expected. 

Conclusion 

The research discussed above suggests that instructional technology can: 

• reduce costs of instruction; 

• increase the accessibility of instruction; 

• increase instructional effectiveness for analysts; 

• reduce the time analysts need to learn facts, concepts, and procedures; 

• track progress and ensure that all learners achieve instructional targets; 

• provide opportunities for helping analysts to compress experience and 

achieve the higher cognitive levels of mastery demanded by their craft. 

In addition, the findings suggest a rule of “thirds.” This rule posits that the 

present state-of-the-art in instructional technologies can reduce the cost of 

instruction by about a third and either increase achievement by about a third or 

decrease time to reach instructional objectives by a third. Eventually, instructional 

technology should provide a conversation between the analyst and the 

technology that will tailor instruction in real time and on demand to the particular 

knowledge, skills, abilities, interests, goals, and needs of each individual. This 

capability, now available in rudimentary forms, can be expected to improve and 

develop with time. Even in its current state of development, however, instructional 

technology deserves serious attention within the Intelligence Community. 

38 

For example, BIP and EXCHECK, respectively. For the first, see A. Barr, M. Beard, and R. C. 

Atkinson, “A rationale and description of a CAI Program to teach the BASIC Programming Language”; 

for the second, see P. Suppes and M. Morningstar. 

39 

A. C. Graesser, M. A. Gernsbacher, and S. Goldman, Handbook of Discourse Processes. 

96

CHAPTER EIGHT 

Organizational Culture: Anticipatory Socialization and 

Intelligence Analysts 

Stephen H. Konya 1 

Rob Johnston 

I know it sounds silly, but I had this image of James Bond before I 

started working here. The truth is, I just sit in a cubicle, and I write 

reports. 

Every organization has a unique culture that is defined partly by its individual 

members and partly by its structure, history, and policies. For that culture 

to endure, it must be transmitted from current members to new members. This 

process, known as organizational socialization, is especially important in 

organizations with strong, insular cultures, as those with weak cultures have 

less to transmit and will tend to experience culture changes as members come 

and go. 

Although socialization begins prior to a person’s first day on the job and is 

a continuous process, it is experienced most intensely by new employees. The 

cultural symbols acquired and interpreted during their initial interaction with 

the institution create potent and lasting impressions. 2 For them, socialization 

1 

Stephen Konya is a Research Associate at the Institute for Defense Analyses, currently examining 

multimodal interfaces for the dismounted for the DARPA/Army Future Combat Systems 

program. He holds an MS in industrial and organizational psychology from Rensselaer Polytechnic 

Institute. 

2 

Umberto Eco, A Theory of Semiotics; Clifford Geertz, The Interpretation of Cultures; Jacques 

Lacan, Ecrits; Ferdinand de Saussure, Course in General Linguistics. 

97

CHAPTER EIGHT 

is the process of learning the ropes; training; and becoming formally and 

informally acquainted with what is actually of value within the organization. 3 

It is also the time when one learns the organization’s norms and taboos and the 

extent of its social capital. 4 In sum, formal and informal socialization are types 

of control mechanism for maintaining the norms, or status quo, within any 

organization. 5 

Organizational Socialization 

According to Daniel Feldman, organizational socialization is “the process 

through which individuals are transformed from outsiders to participating, 

effective members of an organization.” 6 As shown in Figure 1, Feldman 

divides this process into three stages: getting in (or anticipatory socialization), 

breaking in (or accommodation), and settling in (often referred to as role management). 

During the getting-in stage, potential employees try to acquire 

information about an organization from available sources, such as Web sites, 

professional journals, and corporate annual reports. The breaking-in stage 

includes orientation and learning organizational as well as job-related proce- 

Feldman’s three stages of organizational socialization. 

dures. The settling-in stage concludes when an individual attains full member 

status in the organization. 

While each of the three stages of socialization is important, the focus of this 

chapter is on the first, or anticipatory, stage. There are several reasons for this. 

Clearly, the expectations people develop about an organization they are joining 

are important to a new recruit’s eventual satisfaction, retention, and performance. 

Moreover, because it can control several aspects of the recruitment 

process, this stage is often the easiest for an organization to change. This 

chapter will take both a descriptive and prescriptive approach to easing the 

socialization of new employees. 

3 

William G. Tierney and Robert A. Rhoads, Faculty Socialization as Cultural Process. 

4 

See footnote 7 in Chapter Two. 

5 

John P. Wanous, Organizational Entry. 

6 

Daniel C. Feldman, “The Multiple Socialization of Organization Members.” 

98

ORGANIZATIONAL CULTURE 

Anticipatory Socialization 

Anticipatory socialization encompasses all of the learning that occurs prior 

to a recruit’s entering on duty. 7 At this stage, an individual forms expectations 

about the job and makes decisions about the suitability of fit between himself 

and the organization. What a person has heard about working for a particular 

organization, such as an intelligence agency, provides an idea of what to 

expect if hired. Conversely, individuals who do not believe they would fit in 

may decide not to apply. 

There are two variables that are particularly useful for tracking a potential 

employee’s progress through the anticipatory stage: The first is realism, or the 

extent to which an individual acquires an accurate picture of daily life in the 

organization. Realism is influenced by the level of success recruits achieve 

during the information-sharing and information-evaluation part of their 

recruitment. The second is congruence, or the extent to which the organization’s 

resources and the individual’s needs and skills are mutually satisfying. 

Congruence is influenced by the level of success an individual has achieved in 

making decisions about employment. Although it cannot directly influence 

congruence, which is an inherently personal experience, an organization can 

present relevant information in order to provide a realistic and accurate 

description of the work performed and the work environment. 

Organizations often use interviews to begin the socialization of new 

recruits. For example, an interviewer will attempt to provide an accurate 

description of what to expect from the job and the organization, the purpose 

being to reduce the likelihood that a recruit will be disturbed by unanticipated 

situations. Interviewing is also used to determine the degree to which there is a 

match between the values of potential recruits and the values of the organization. 

New recruits with personal values matching those of the organization 

have been found to adjust to the organization’s culture more quickly than 

recruits with nonmatching values. 8 

Organizations also send cultural messages to new recruits during interviews. 

When there are several rounds of interviews with progressively senior 

members of the organization, for example, the message conveyed is that finding 

the best person for the position is important. In contrast, hiring for a parttime 

job at the lowest level of the organization is often accomplished quickly, 

to the extent that a person having minimally acceptable qualifications may 

7 

This stage is termed “pre-arrival” in Lyman W. Porter, Edward E. Lawler, and J. Richard Hackman, 

Behavior in Organizations. 

8 

Jerald Greenberg and Robert A. Baron, Behavior in Organizations: Understanding the Human 

Side of Work. 

99

CHAPTER EIGHT 

often be hired on the spot. The cultural message in this case is that such 

employees are easily let in to and out of the organization. 

Another, particularly pertinent example is intelligence work, which requires 

that recruits undergo employment screenings unlike those found in most civilian 

jobs. Potential CIA analysts must submit to a thorough background investigation, 

a polygraph examination, and financial and credit reviews. Further, a battery 

of psychological and medical exams must be passed prior to a formal 

employment offer. The timeframe for the background check eliminates the possibility 

of a rapid hiring decision. Even more important are the nonverbal messages 

sent to the recruit that this is a position of secrecy and high importance. 

Several sources of information contribute to beliefs about any organization. 

Friends or relatives who are already part of the organization might share their 

experiences with the person considering employment. Information might also 

be acquired from other sources, such as professional journals, magazines, 

newspaper articles, television, governmental and private Web sites, public 

statements or testimony, and annual reports. While these sources of information 

about an organization are far from perfect (all may contain positive and 

negative hyperbole), they are still useful from the point of view of forming 

preliminary ideas about what it might be like to work for that organization. 

Because competition for highly qualified employees is fierce, successful 

recruitment usually involves a skillful combination of salesmanship and diplomacy. 

Recruiters tend to describe their organizations in glowing terms, glossing 

over internal problems and external threats, while emphasizing positive 

features. The result is that potential employees often receive unrealistically 

positive impressions of conditions prevailing in a specific organization. When 

they arrive on the job and find that their expectations are not met, they experience 

disappointment, dissatisfaction, and even resentment that they have been 

misled. In fact, research findings indicate that the less employees’ job expectations 

are met, the less satisfied and committed they are and the more likely 

they are to think about quitting or actually to do so. 9 

These negative reactions are sometimes termed entry shock, referring to the 

confusion and disorientation experienced by many newcomers to an organization. 

In order to avoid entry shock, it is important for organizations to provide 

job candidates with accurate information about the organization. Research 

supports the notion that people exposed to realistic job previews later report 

higher satisfaction and show lower turnover than those who receive glowing, 

but often misleading, information about their companies. 10 Moreover, having 

9 

John P. Wanous et al., “The Effects of Met Expectations on Newcomer Attitudes and Behavior: 

A Review and Meta-analysis.” 

10 

Bruce M. Meglino et al., “Effects of Ralistic Job Previews: A Comparison Using an Enhancement 

and a Reduction Preview.” 

100


realistic expectations helps to ease the accommodation stage of the socialization 

process. 

Consequences of Culture Mismatch 

When I got here, I felt like a rabbit stuck in headlights. Now, I feel 

like a deer. 

It took me a while to figure out that this place runs more like a 

newspaper than a university. 

It’s pretty solitary work. I spend all day in my head. I really wasn’t 

expecting that. 

There are several consequences of a cultural mismatch between an 

employee and an organization. Among these consequences are culture shock, 

low job satisfaction, low employee morale, increased absenteeism, increased 

turnover, and increased costs. 

Culture Shock. People often have to be confronted with different cultures 

before they become conscious of their own culture. In fact, when people are 

faced with new cultures, it is not unusual for them to become confused and 

disoriented, a phenomenon commonly referred to as culture shock. 

Beryl Hesketh and Stephen Bochner, among others, have observed that the 

process of adjusting to another culture generally follows a U-shaped curve. 11 

At first, people are optimistic about learning a new culture. This excitement is 

followed by frustration and confusion as they struggle to learn the new culture. 

After six months or so with the organization, people adjust to their new 

cultures, become more accepting of them, and are more satisfied by them. For 

those who enter a mismatched culture, the productivity issue is clear: the several 

months required to adjust and accept the new work style results in several 

months of even lower productivity than is obtainable with those who fit in 

right away. 

Job Satisfaction. Job satisfaction is defined by one scholar as “people’s positive 

or negative feelings about their jobs.” 12 It is hardly surprising that dissatisfied 

employees may try to find ways of reducing their exposure to their jobs. 

This is especially significant when one considers that people spend roughly 

one-third of their lives at work. 

11 

Beryl Hesketh and Stephen Bochner, “Technological Change in a Multicultural Context: Implications 

for Training and Career Planning”; Maddy Janssens, “Interculture Interaction: A Burden 

on International Managers?” 

12 

Edwin A. Locke, “The Nature and Causes of Job Satisfaction.” 

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CHAPTER EIGHT 

Interestingly, research suggests that the relationship between satisfaction 

and task performance, although positive, is not especially strong. 13 Thus, 

while job satisfaction may be important to the longevity of any individual 

career cycle, it is not a major factor in individual job performance. It does, 

however, increase absenteeism, which has a negative effect on overall organizational 

productivity. 

Absenteeism and Turnover. Research indicates that the lower an individual’s 

job satisfaction, the more likely he or she is to be absent from work. 14 As 

with job satisfaction and task performance, this relationship is modest but also 

statistically significant. An employee may even choose to leave an organization 

altogether. This voluntary resignation is measured as employee turnover 

and has fiscal consequences for both the individual and the organization. 

Fiscal Cost. Employee turnover is a critical cost element. The expense of 

recruiting and training new employees, along with lost productivity from 

vacant positions and overtime pay for replacement workers, increases operating 

costs and also reduces employee organizational output. 

A 2002 study by the Employment Policy Foundation found that the estimated 

turnover cost is $12,506 per year per full-time vacancy for the average 

employee with total compensation (wages and benefits) of $50,025. 15 As the 

average annual turnover benchmark within the Fortune 500 is 23.8 percent, 

one can clearly see how critical it is for organizations to lessen the number of 

employees who leave voluntarily. Even unscheduled absences can be expensive—averaging 

between $247 and $534 per employee, per day, according to 

the same study. 

Anticipatory Socialization in the Intelligence Community 

The secrecy is strange. I thought it would be romantic, but it turns 

out that it is just strange. 

I was sold on the cool factor. It’s still sort of cool, I guess. 

Accepting a job with one of the 14 members of the Intelligence Community 

differs from other professions in that it is difficult for new employees to have a 

clear and precise understanding of the roles and responsibilities they are about 

13 

The correlation is 0.17 according to Michelle T. Iaffaldano and Paul M. Muchinsky in their 

“Job Satisfaction and Job Performance: A Meta-Analysis.” 

14 

Lyman W. Porter et al., “Organizational Commitment, Job Satisfaction and Turnover Among 

Psychiatric Technicians.” 

15 

This number does not take into account the additional costs within the Intelligence Community 

for background and security investigations. 

102


to assume. This is all the more pronounced because, for the most part, the 

Intelligence Community organizations lack a civilian counterpart. 

Occasionally, the anticipatory socialization of people entering the intelligence 

analysis discipline will derive from accounts of current or former practitioners. 

More generally, however, a newcomer’s initial impressions stem from the fictional 

media portrayals, which tend to emphasize the supposed glamour of operational 

tasks and pay little attention to the reality of research-based analytic 

work. The absence of hard knowledge about intelligence work is attributable, in 

part, to the organizational secrecy of the Intelligence Community and, in part, to 

the actual socialization process that occurs after one has been accepted for 

employment and has passed the required background investigation. 

A newcomer’s experience is often contrary to initial expectations. Employees 

are discouraged from talking about the specifics of their work outside of 

the organization or with those who have not been “cleared.” On an individual 

level, this experience translates into professional culture shock and social isolation. 

Organizationally, an intentionally closed system of this kind has a 

number of potential performance-related consequences, among them perpetuation 

of the existing organizational culture by hiring familial legacies or those 

most likely to “fit in,” job dissatisfaction, low morale and consequent reduction 

in employee readiness, increased employee turnover, greater likelihood of 

“groupthink,” and strong internal resistance to organizational change. 16 

Since the attacks of 11 September, the Intelligence Community has become 

more open about its role in government, its day-to-day working environment, 

and its employees’ functions and responsibilities. While this openness is an 

extension of an ongoing trend toward public outreach—an example is the 

CIA’s Officer-in-Residence program established in 1985—the community has 

accelerated this trend toward openness in an effort to help the public, and its 

representatives, understand the missions and value of the Intelligence Community. 

17 

This trend toward openness has improved employee retention by counteracting 

the culture shock of misinformed anticipatory socialization and resultant 

employee turnover. This trend also helps prepare the organization for the 

inevitable changes to come by increasing the potential recruitment pool, 

expanding the intellectual diversity of its staff, and fostering better relations 

with its broader constituency, the American public. 

16 

Irving Janis, Groupthink. 

17 

See CIA Officer in Residence Program in Web Resources in bibliography. 

103

CHAPTER EIGHT 

Conclusion and Recommendations 

As noted, there is something of a disconnect between the largely fictionalized 

portrayal of the Intelligence Community in the popular media and the 

actual experience of intelligence analysts. This disconnect can be exacerbated 

once a recruit is on the job and can lead to negative consequences and behaviors, 

such as organizational culture shock, employee dissatisfaction, and 

increased employee absenteeism and turnover. This has an obvious effect on 

individual analysts, but it has a direct effect on the efficiency and effectiveness 

of the Intelligence Community. 

Since the September 2001 attacks, some members of the Intelligence Community 

have acted to change the socialization process by providing accurate 

and realistic career information. One of the most widely used media for this is 

the Internet. For example, the Central Intelligence Agency’s (CIA) Web site 

contains a section on “Life at the CIA.” 18 This section contains information 

about the Agency and its culture, several analyst profiles and job descriptions 

written in the analyst’s own words, and information concerning employee 

benefits and social and intellectual diversity. Although the “Employment” 

section of the Federal Bureau of Investigation’s (FBI) Web site is less detailed 

than the “Life at the CIA” section of the CIA Web site, it does illustrate a typical 

first assignment. 19 In contrast, the “Careers” section of the Defense Intelligence 

Agency’s (DIA) Web site contains detailed information on current job 

openings and the application process, but it provides no information about the 

actual work of a DIA analyst. 20 Steps such as these are encouraging, but they 

are still insufficient. There are more active things that can be done to facilitate 

the socialization of new employees. 

To begin, Intelligence Community components should accept that what 

most people know about a job is often false and that it is incumbent on the 

organization and its recruiters to present accurate pictures and to work diligently 

to dispel myths. This will help to counteract the effects of culture 

shock. Instead of overselling a particular job or organization, recruiters should 

focus on facilitating the anticipatory socialization of potential employees by 

providing accurate information about the job and about the culture of the organization 

itself. Early in the selection process, applicants should be provided 

with realistic job previews, presented in either written or oral form. Previews 

should contain accurate information about the specific conditions within an 

organization and the specific requirements of the job. Research has shown that 

providing accurate descriptions of tasks is important in increasing job commitment 

and job satisfaction, as well as decreasing initial turnover of new 

18 

See Central Intelligence Agency Web site in Web Resources. 

19 

See Federal Bureau of Investigation Web site in Web Resources 

20 

See Defense Intelligence Agency and US Intelligence Community Web sites in Web Resources. 

104


employees. 21 The job preview allows candidates to make an informed decision 

to continue with the recruitment process or to withdraw from it if they feel the 

job is not appropriate. Realistic previews also lower unrealistically high 

expectations. A particularly good example of such an effort can be found on 

the CIA’s Office of General Counsel Web site. This Web site includes a section 

titled “Misconceptions about working for the CIA,” which tries to dispel 

prejudices and biases about employment at the CIA by addressing them in a 

straightforward manner. 22 In addition, the authors explain the benefits of having 

work experiences with the CIA for future employment endeavors in other 

areas. 

Interview screenings of applicants should be reviewed and improved where 

needed. Hiring interviews are not very effective predictors of job performance; 

even so, there are ways to improve their reliability and validity. 

Numerous cognitive measurement instruments are available that help predict a 

match between an individual’s knowledge, skills, and abilities and specific 

behavioral, cognitive, and psychomotor tasks. 23 In addition, the use of structured 

interviewing - posing the same questions to all applicants - is more 

effective than unstructured interviewing. Structured interviews allow for consistent 

comparisons among applicants. 24 Organizations should also consider 

using panel interviews. Differences among individual interviewers may result 

in inaccurate judgment of an applicant, but the overall decision of a team of 

evaluators may improve reliability. 25 

The use of situational exercises should be included in the recruitment process. 

These exercises usually consist of approximations of specific aspects of 

a job. They can be used to evaluate candidates’ job abilities and to provide 

candidates with simulated work tasks. The former can facilitate organizational 

evaluations of candidates’ performance on a job-related task; the latter may 

help candidates to decide whether the job would be a good match. 26 

A desirable additional step would be the creation and expansion of academic 

degree programs with a focus on intelligence and intelligence analysis. 

Further, an enhanced effort to improve public awareness and understanding of 

the Intelligence Community through greater community outreach, internships, 

21 

Glenn M. McEvoy and Wayne F. Cascio, “Strategies for Reducing Employee Turnover: A 

Meta-analysis.” 

22 

See Central Intelligence Agency, Office of General Counsel Web site. 

23 

The Buros Institute of Mental Measurements tracks and reports the statistical validity and reliability 

of thousands of measurement instruments. 

24 

Richard D. Arvey and James E. Campion, “The Employment Interview: A Summary of Recent 

Research.” 

25 

P. L. Roth and James E. Campion, “An Analysis of the Predictive Power of the Panel Interview 

and Pre-Employment Tests.” 

26 

Wayne F. Cascio, Applied Psychology in Human Resource Management. 

105

CHAPTER EIGHT 

research fellowships, professional workshops, and academic forums will help 

to facilitate better employee relations by providing potential employees with a 

clearer perspective on what to expect after receiving their badge. 

106

CHAPTER NINE 

Recommendations 

The First Step: Recognizing A Fundamental Problem 

It is far too early in the research process to determine if any one organizational 

model for the Intelligence Community is more or less effective than any 

other, but I believe there is a fundamental structural question that needs to be 

addressed at the outset. This is, in my view, that current reporting competes 

for time and resources with indications and warning (I&W) intelligence. This 

emphasis is unlikely to change, for several reasons. First, current intelligence 

reporting results in significant “face-time” for the Intelligence Community 

with policy makers, who, in turn, provide the resources that fund and support 

community activities. This is a significant contributor to the social capital that 

the Intelligence Community commands. 

The second reason is that in-depth research of the kind that contributes to 

I&W intelligence is a long-term investment whose payoff is often an abstraction. 

Not infrequently, successful warnings are taken for granted. Those that 

fail, however, may well involve the community in public recriminations that 

cost the Intelligence Community significant social capital. In this sense, the 

Intelligence Community’s focus on current reporting is understandable. The 

problem is that producing current intelligence tends to become an all-consuming 

activity. The majority of analysts who participated in this study said that 

their time was spent on current reporting. Unfortunately, this does little to 

improve I&W intelligence, which requires long-term research, in-depth expertise, 

adoption of scientific methods, and continuous performance improvement. 

The return for the Intelligence Community, in terms of social capital, 

may be quite limited and even, as noted above, negative. Thus, the analytic 

area most in need of long-term investment often gets the least. 

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CHAPTER NINE 

As the resources available to intelligence analysis are limited, it needs to be 

determined if those resources are better spent on the reporting functions of the 

Intelligence Community or on warning functions. It also needs to be determined 

whether these functions should be performed by the same analysts or if 

they are two separate career tracks. To make this determination, the Intelligence 

Community will need to invest in what I call a Performance Improvement 

Infrastructure as well as basic and applied analytic research. 

Performance Improvement Infrastructure 

The first step in improving job or task-specific performance is the establishment 

of a formal infrastructure designed explicitly to create an iterative performance 

improvement process. Such a process would include: 

• measuring actual analytic performance to create baseline data; 

• determining ideal analytic performance and standards; 

• comparing actual performance with ideal performance; 

• identifying performance gaps; 

• creating interventions to improve analytic performance; 

• measuring actual analytic performance to evaluate the effectiveness of 

interventions. 

Several organizational, or infrastructure, assets should be developed to support 

this process. These should include: 

• basic and applied research programs; 

• knowledge repositories; 

• communities of practice; 

• development of performance improvement techniques. 

The performance improvement process would be repeated throughout the 

life cycle of an organization in order to encourage continuous improvement. 

With the infrastructure and process in place, an organization would be capable 

of adapting to new or changing environmental conditions. 

Infrastructure Requirements 

Institutional changes, such as corporate reorganizations, are often enacted 

without a clear understanding of their potential or actual impact. What is most 

often missing in such changes is a basic research plan or a systems approach 

108

RECOMMENDATIONS 

to determine and predict the effect on organizational performance. The same 

is true with the Intelligence Community. Although there have been numerous 

proposals to reorganize the Intelligence Community—including those that 

resulted from the hearings of the Kean 9/11 commission—few have addressed 

the question of why one change would be any more effective than any other 

change. Merely asserting, based on some a priori notion of effectiveness, that 

organizational scheme X is more effective than organizational scheme Y is 

insufficient evidence. What is needed is a posteriori data, such as case studies, 

to support or refute the proposed change. 1 

Organizational Requirements. Many large organizations distribute performance 

improvement responsibilities throughout the organization at a supervisory 

or midlevel of management, but the group most often charged with 

collecting and analyzing performance data is the human resources department. 

This task generally involves developing task-specific performance standards 

and metrics based on expert performance models and in accordance with corporate 

policy. 

The human resources department also becomes the central repository for 

pre-, periodic, and post-performance measurements. As this department generally 

has contact with employees throughout their careers, this is the most 

efficient way to manage, analyze, and inform senior leadership about aggregate 

changes in performance over time. Although data are collected at the 

individual level, it is the aggregation of performance data that allows leadership 

to determine the effectiveness of any organizational change or job-related 

intervention. 

Baseline Data. Measuring actual analytic performance is essential to the 

establishment of a data driven performance infrastructure. The analysts in this 

study perceived their performance to be tied directly to the quantity of written 

products they produced during each review period. Counting the number of 

analytic publications is one metric, of course, but it is hardly indicative of analytic 

quality. Surgeons are a useful example of this problem.. They may 

count the number of patients they treat, but this metric says more about system 

throughput and salesmanship than it does about surgical performance. Unlike 

the purely cognitive work of intelligence analysts, surgeons have the advantage 

of multiple physical outputs, which makes measurement an easier task. 

In particular, surgeons have patient outcomes, or morbidity and mortality 

ratios, which become a grounded end-state for all measurements. 2 Other 

things being equal, these data then ought to inform a prospective patient about 

where to take his or her business. 

1 

William Nolte, a deputy assistant director of central intelligence for analysis and production proposed 

such an idea in “Preserving Central Intelligence: Assessment and Evaluation in Support of 

the DCI ” in Studies in Intelligence 48, no. 3 (2004): 21–25. 

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CHAPTER NINE 

For intelligence analysts, the question may be put as, “What is an analytic 

morbidity and mortality ratio?” The process of describing and identifying 

morbidity and mortality, or error and failure in analytical terms, is a necessary 

step in identifying mechanisms to develop, test, and implement performance 

improvement interventions. There was little consensus among the participants 

in this study about what comprises failure, or even if failure was possible. 

There was greater consensus regarding the nature of analytic error, which was 

generally thought to be a consequence of analytic inaccuracy. 

Metrics. One could reasonably conclude that compounded errors lead to 

analytic failure. Conversely, one could conclude that failure is the result of 

analytic surprise, that its causes are different from the causes of error, and that 

it needs to be treated as a separate measurement. This subject is open to 

debate and will require further research. It is still possible, however, to use 

both accuracy and surprise as metrics in evaluating analytic performance on a 

case-by-case basis. 

The advantage of an error and failure metric is that it is observable in a 

grounded state separate from the analytic process. Any analytic product can 

be reviewed to determine levels of accuracy, and any unexpected event can be 

traced back through analytic products to determine if there was an instance of 

surprise. 

Once levels of error and failure are calculated, along with measures of output, 

it is possible to determine expert levels of performance and to derive performance 

models based on successful processes. In any organization, there 

will be those individuals with the greatest output—in this case, the greatest 

number of written products. There will also be individuals with the highest 

levels of accuracy—in this case, factual consistency. There will also be individuals 

who have the lowest incident of surprise—in this case, those who generate 

the greatest number of potential scenarios and track and report 

probabilities most reliably. Using data-driven metrics means that expertise is 

not a function of tenure; rather, it is a function of performance. 

Once expert performers are identified, it is possible to capture their work 

processes and to develop performance models based on peak efficiency and 

effectiveness within the Intelligence Community. Through the use of cognitive, 

behavioral, and linguistic task analyses, ethnography, and controlled 

experiments, it is possible to generate process metrics to identify analytic 

methods that are more effective for specific tasks than other methods. This is 

not to say that there is one analytic method that is the most effective for intel- 

2 

Grounded Theory is the development of theoretical constructs that result from performing interpretive 

analysis on qualitative data rather than relying on a priori insights. The theory is then derived 

from some grounded data set. Barney Glaser and Anselm Strauss, Discovery of Grounded Theory; 

Barney Glaser, Theoretical Sensitivity; Barney Glaser, Basics of Grounded Theory Analysis. 

110


ligence analysis; rather, each type of task will have an analytic method that is 

best suited to accomplishing it in an efficient and effective manner. 

Developing these metrics is no small task. It is a job that will require 

numerous researchers and research programs within, or with access to, the 

Intelligence Community. These programs will need formal relationships with 

human resource departments, analytic divisions, organizational leadership, 

and developers of training and technology interventions in order to have a 

positive effect on analytic performance. 

Research Programs 

The results of this research indicate that the Intelligence Community needs 

to commit itself to performance research that is rigorous, valid (in that it measures 

what it proposes to measure), and replicable (in that the method is sufficiently 

transparent that anyone can repeat it). Within some intelligence 

organizations, this has been an ongoing process. The problem is that most of 

the internal research has concentrated on historical case studies and the development 

of technological innovations. What is missing is focused study of 

human performance within the analytic components of the Intelligence Community. 

Questions about the psychology and basic cognitive aptitude of intelligence 

analysts, the effectiveness of any analytic method, the effectiveness of 

training interventions, group processes versus individual processes, environmental 

conditions, and cultural-organizational effects need to be addressed. 

This effort will require commitment. Researchers will have to be brought 

into the Intelligence Community, facilities will have to be dedicated to 

researching analytic performance, expert analysts will have to give some percentage 

of their time to participating in research studies, managers and supervisors 

will have to dedicate time and resources to tracking analytic 

performance within their departments, human resource staffs will have to dedicate 

time and resources to developing a performance repository, and there 

will have to be formal interaction between researchers and the community. 

Analytic Performance Research. In the previous section, I discussed the 

need for analytic standards as part of the Performance Improvement Infrastructure. 

In terms of a research program, this will require, as a first step, the 

collection of baseline analytic performance data and a clear and measurable 

description of ideal analytic behavior. Next, there should be a determined 

effort by human performance researchers to develop, test, and validate analytic 

performance metrics and measurement systems. This will be a lengthy 

process. The accuracy and surprise measures suggested in this text require 

large historical and comparative data sets and are cumbersome and time consuming 

to perform. Conducting behavioral, cognitive, and linguistic task 

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CHAPTER NINE 

analyses requires significant research expertise, ample time, and broad organizational 

access. 

In time, analytic performance research will become a highly specialized 

domain and will require continuous organizational access not normally available 

to outsiders. It will become necessary for the Intelligence Community to 

establish internal or cooperative research centers in order to acquire the 

research expertise necessary to analyze and effect performance improvement. 

There are numerous community outreach efforts on which these centers 

can be built. Those efforts need to be expanded, however, and those programs 

need to include domains beyond the traditional relationship between the Intelligence 

Community and political or geographic area experts. 3 

Institutional Memory. The results of this research program indicate that 

there is a loss of corporate knowledge in the Intelligence Community due to 

employee attrition and the lack of a central knowledge repository for capturing 

“lessons learned.” A number of industries and government organizations, 

including the Departments of Defense and Energy and the National Aeronautics 

and Space Administration, already maintain centers for lessons learned as 

an information hub for its employees. 4 

These centers act as information repositories for successful and unsuccessful 

operations and interventions. Their purpose is to reduce the amount of 

organizational redundancy and levels of error and failure by tracking, analyzing, 

and reporting on after-action reviews and analytic outcome data. 5 The 

other primary function of these repositories is to establish networks for communities 

of practice within and among organizations. 

Networked communities of practice allow professionals to interact, exchange 

methodological information, post and respond to individual case studies, and 

develop ad hoc teams of experts for specific problem solving tasks. With simple 

search tools, basic database software, and a simple network visualization interface, 

any analyst in the Intelligence Community would be able to identify any 

other expert whose domain specialty was needed to answer a specific question 

or solve a specific problem. Another advantage of this model is the development 

of formal and informal mentoring within the network. Any novice would 

be able to find an expert within the Intelligence Community and establish a relationship 

that would be beneficial to both. With appropriate incentives, experts 

would be encouraged to contribute to the network and make available their time 

and expertise for the purpose of mentoring. 

3 

An example of researching the validity and reliability of metrics can be found in the Buros Mental 

Measurement Yearbook at the Buros Institute of Mental Measurements Web site. 

4 

See the US Army Center for Army Lessons Learned (CALL) Web site, which has links to 

numerous other repositories. 

5 

See Chapter Six for a more detailed explanation of the After Action Review process. 

112


Intelligence analysis, like other fields of science, is a cognitive process. 

Although tools and technologies may be available to assist cognitive processes, 

such as measurement devices for physical scientists, technology is ultimately 

merely a tool to be designed and developed using a human-centered 

approach. As such, any new technology needs to be a passive tool, employed 

by analysts to solve specific problems or answer specific questions, rather 

than a restrictive reinterpretation of cognition according to the rules of binary 

computation and artificial intelligence theorists. 6 

Analytic Psychology and Cognition. As evidenced by the work of Richards 

Heuer and others, there is significant research to be conducted into the cognitive 

mechanisms involved in intelligence analysis. 7 Understanding and defining 

the heuristics used in performing intelligence analysis, as well as 

cognitive-load thresholds, multitasking requirements, mechanisms that generate 

cognitive biases, and the utilization of pattern recognition strategies and 

anomaly detection methods are all areas that will prove fundamental to 

improving analytic performance. 

In addition to researching basic cognitive functions and intelligence analysis, 

this area of research will be valuable for understanding how external variables, 

such as time constraints and analytic production methods, affect the 

cognitive processing of individual analysts. Another result will be the development 

of future employee screening and selection tools that will match the 

specific cognitive requirements of intelligence analysis with each applicant’s 

individual knowledge, skills, and abilities. 

Analysts employ cognitive strategies that are time efficient in order to cope 

with the demands of producing daily written products, but such strategies are 

not necessarily the most effective analytic methods for increasing analytic 

accuracy and decreasing the occurrence of analytic surprise. In fact, improving 

analytic accuracy and avoiding surprise may require mutually exclusive 

analytic strategies. This line of inquiry will require baseline performance data 

generated through the development of performance metrics and conducted in 

conjunction with research in analytic methodology effectiveness. The results 

would then be integrated into a knowledge repository. 

These types of studies will require experimental psychologists and cognitive 

scientists working in controlled laboratory environments with consistent 

access to working professional analysts. 

6 

See Chapter Five for a more detailed description of the limitations of technological solutions. 

7 

Richards J. Heuer, Jr., Psychology of Intelligence Analysis; William Brei, Getting Intelligence 

Right; Isaac Ben-Israel, “Philosophy and Methodology of Intelligence: The Logic of Estimate 

Process”; Klaus Knorr, Foreign Intelligence and the Social Sciences; Abraham Ben-Zvi, “The 

Study of Surprise Attacks.” See also Marjorie Cline, Carla Christiansen and Judith Fontaine, 

Scholar’s Guide to Intelligence Literature. 

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CHAPTER NINE 

Development and Validation of Analytic Methods. The Intelligence Community 

routinely generates ad hoc methodologies to solve specific analytic 

problems or crises. However, once the problem has been solved, or the crisis 

averted, the new analytic method may or may not become part of the institutional 

memory. Often these new methods are lost and need to be re-created to 

address the next problem or crisis. In addition, these methods are seldom 

tested against other competing analytic methods for validity or reliability. It is 

difficult for an analyst to know which analytic method to employ in a given 

situation or requirement. 

There are obvious inefficiencies in the current model. First, there is the loss 

of corporate knowledge each time an innovative analytic method is generated 

and subsequently abandoned. Second, there is no effectiveness testing center 

where analytic methods can be compared for specific cases. Although there 

are hundreds of analytic strategies, there is no way to determine which strategy 

is the most effective for any particular problem set. 

The lack of an analytic methodology research agenda leads analysts to 

choose methods with which they are most familiar or to choose those dictated 

by circumstance, such as deadlines. Moreover, instead of advancing the concept 

that intelligence analysis is science and needs to be engaged in like any 

other scientific discipline, the paucity of effectiveness data supports a deepseated 

community bias that analytic methods are idiosyncratic and, therefore, 

akin to craft. 

The development of a research agenda for analytic methodology that is 

focused on collecting effectiveness and validation data is the first step in moving 

intelligence analysis from a tradecraft model to a scientific model. This 

may be the most culturally difficult recommendation to implement: there is 

cultural resistance to adopting a science-based model of intelligence analysis 

that is rooted in the traditions, norms, and values of the Intelligence Community. 

Another difficult step will be to introduce effectiveness data and corresponding 

analytic methods to the community at large and to incorporate these 

in future training programs. 

Training Effectiveness. Successful analysis demands group cohesion and 

the implementation of consistent, effective analytic methods within the Intelligence 

Community. The best way to achieve this is through formal basic and 

advanced training programs. As noted earlier, several agencies within the 

community have invested resources in formal training programs, but these 

programs are unique to each agency and are often missing evaluations of student 

performance. Although most formal courses include a written subjective 

evaluation of the instructor, as well as the student’s perception of the value of 

the course, the evaluation of student performance has yet to be formalized. 

114


Without evaluating preintervention, or precourse, performance and following 

that with a postintervention evaluation, it is difficult to determine the effect that 

any training intervention will have on employee performance. In addition to 

formal measurements based on course objectives, it is important to collect performance 

data from managers and supervisors to evaluate the retention of training 

and the impact that training has had on actual day-to-day performance. 

Developing performance metrics will inform and advance the training interventions 

currently employed in the Intelligence Community and will determine 

the gap between ideal performance and actual performance. As such, 

the system is an iterative process of setting performance standards, measuring 

actual performance, designing training interventions to improve performance, 

and evaluating the effects of those interventions on actual performance. The 

data derived from these interventions and measurements will then contribute 

to the growth of the knowledge repository and strengthen the ties created 

through the communities of practice. 

Organizational Culture and Effectiveness. Identifying existing organizational 

norms and taboos is the first step to creating an internal dialogue about 

the future of an organization and its place in a competitive environment. Culture 

drives the operations of an organization, determines the people who are 

hired, enculturates new employees, establishes standards of behavior and systems 

of rewards, shapes an organization’s products, and determines the social 

capital that any organization may possess. In short, culture defines an organization’s 

identity to itself and to others. 

Understanding the culture of the Intelligence Community and analyzing the 

effects of any performance intervention on that culture contributes to the evaluation 

of intervention effectiveness. Effective performance interventions will 

have a positive effect on the organization’s culture and become themselves 

measurement instruments. 

Developing cultural markers to track organizational change and performance 

improvement requires baseline ethnographic data and the identification 

of key cultural indicators. Once identified, cultural indicators such as language 

use, norms, and taboos would be measured at regular intervals and 

would serve as grounded data to determine levels of change within the organization. 

This would permit interventions to be modified before they became 

ritualized within the Intelligence Community. 

The Importance of Access 

The improvement of human performance often requires an organization to 

change its culture, and organizational leaders seldom possess sufficient power 

to mandate cultural change by edict. At best, management can introduce 

115

CHAPTER NINE 

agents or agencies of change and manage their organization’s culture in the 

same way they manage physical and financial resources. An organization’s 

culture shapes individual behavior by establishing norms and taboos and, ultimately, 

determines the quality and character of an organization’s products. 

Culture and product are inseparable, and one cannot be changed without 

affecting the other. The choice confronting any organization is to manage its 

institutional culture or to be managed by it. 

There is no single path to carrying out the research recommended in this 

work. It could be performed at a single center or coordinated through several 

specific centers; it could be purely internal to the Intelligence Community; a 

cooperative effort among the community, academe, and national laboratories; 

or some combination of these. What is most important to effective implementation 

is that there be regular and open access among researchers and the Intelligence 

Community. This may appear simple enough, but access equals trust, 

and trust is difficult to establish in any domain. This is especially the case 

within the Intelligence Community. The Intelligence Community needs to 

increase its commitment to community outreach efforts. This study is one 

such effort. 

During the course of my research, the value of access and the premium the 

community places on trust quickly became evident. At agency after agency, 

physical access restrictions, security clearances, forms, interviews, phone 

calls, questions, vetting, and more vetting were all signs of the value, not of 

secrecy per se, but of trust and access. Without this sort of cooperation, this 

research would have been impossible, and this is an important lesson that 

ought to inform future research programs. 

116

PART IV 

Notes on Methodology 

117

CHAPTER TEN 

Survey Methodology 

This study included 489 interviews with intelligence professionals, academics, 

and researchers throughout the Intelligence Community. It also involved 

participation in intelligence training programs, workshops, and focus groups; 

direct observation of intelligence analysts performing their duties, and participant 

observation in a variety of analytic tasks. My access was not restricted to 

specific people, locations, or organizations. I was allowed to observe, interview, 

and participate in whatever manner I thought would be most beneficial 

to the research project. 

Unlike other academic studies of the intelligence discipline (case studies or 

topic-specific postmortems, for example), this study was process oriented. It 

also differed from the work of Sherman Kent, Richards Heuer, and other intelligence 

professionals concerned with the process of intelligence analysis. 1 

Rather than having an intelligence professional looking out to the social and 

behavioral sciences, this study had a social scientist looking in at the intelligence 

profession. Although some of the conclusions of this work may be similar 

to previous studies, the change in perspective has also led to some 

different findings. 

It is important to keep in mind that cultural anthropology is a qualitative 

discipline and that, in general, its findings are descriptive and explanatory 

rather than inferential or predictive. The use of ethnographic methods to 

describe a culture, the environment in which that culture operates, and the 

work processes that culture has adopted is designed to generate testable theory 

that can be investigated experimentally or quasi-experimentally using other 

1 

Sherman Kent, Strategic Intelligence for American World Policy; Richards J. Heuer, Jr., Psychology 

of Intelligence Analysis. 

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CHAPTER TEN 

research methodologies. Additionally, ethnography is used to identify and 

describe the influence of different variables on cultural phenomena, again 

with a focus on developing testable theory. Unlike more quantitative disciplines, 

cultural anthropology is not traditionally employed experimentally to 

test theory or to generate predictive measures of statistical significance. 

The findings in this work describe the data collected during this study, but 

they do not indicate the weight or general statistical effect of any one variable 

as opposed to any other variable. Although a single variable might have more 

effect on the error or failure rate of intelligence analysis, further quantitative 

research will be needed to determine those statistical values. Without additional 

quantitative support, it may not be possible to generalize from these 

findings. 

Methodology 

This study used an applied anthropological methodology for the collection 

and analysis of qualitative data. 2 A traditional approach to ethnography, the 

descriptive documentation of living cultures, was modified for use in postindustrial 

organizational settings. 3 This method included conducting interviews, 

directly observing analysts performing their jobs, participating in analytic 

tasks and training, and conducting focus groups. The settings for this 

research included the 14 members of the Intelligence Community, related 

government agencies, universities, think tanks, national laboratories, the 

National Archives and related presidential libraries, and private and corporate 

locations. 

The background data were collected using a Q-sort literature review method, 

which is discussed in more detail in Chapters Three and Eleven. This procedure 

was followed by semi-structured interviews, direct observation, participant 

observation, and focus groups. The Q-sort method was employed specifically 

because of its utility for developing taxonomic categories. 4 

The identity of the research participants will not be revealed. Participant 

responses and observational data gathered during the research process have 

been tabulated and made anonymous or aggregated according to context and 

2 

Erve Chambers, Applied Anthropology: A Practical Guide; Alexander Ervin, Applied Anthropology: 

Tools and Perspectives for Contemporary Practice. 

3 

Russell Bernard, Research Methods in Anthropology: Qualitative and Quantitative Approaches; 

Robert Bogdan, Participant Observation in Organizational Settings; Norman Denzin and Yvonna 

Lincoln, Handbook of Qualitative Research; Jean Schensul and Margaret LeCompte, Ethnographer’s 

Toolkit. Vol. I - Vol. VII; James Spradley, Participant Observation; Robert Yin, Case 

Study Research: Design and Methods. 

4 

William Stephenson, The Study of Behavior: Q-Technique and its Methodology. 

120

SURVEY METHODOLOGY 

content and, thus, are not attributable to any specific individual. This is not 

simply the result of security procedures within the Intelligence Community; it 

is also the professional obligation of every member of the American Anthropological 

Association, as stated in the American Anthropological Association 

Code of Ethics. 5 

The interview technique employed in this study was semi-structured. Several 

specific questions about the participant’s perception of the nature of intelligence, 

the analytic process, the intelligence production cycle, and 

intelligence errors and failures were standard throughout the interviews. Other 

questions, specific to the individual’s job responsibilities, were tailored to 

each respondent. This method allowed for a more open-ended approach, 

which surveys and highly structured interviews do not. The semi-structured 

method is more akin to an open conversation (with consistent data collection 

constructs and probing questions) than to a formal interview, which helps put 

the respondents at ease and makes the entire process seem somewhat less contrived. 

Access to interview participants was made possible through the Center for 

the Study of Intelligence. Individuals at CSI introduced me to their contacts 

throughout the Intelligence Community, including active and retired senior 

analysts, managers and senior leadership, as well as to academics and 

researchers. The various intelligence-training centers put me in touch with 

new hires and novice analysts. Each interviewee was asked to make recommendations 

and provide contact information for others who might be interested 

in participating in this research project. In addition, numerous 

interviewees were approached without a formal or informal introduction from 

a previous participant. Only four of the 489 individuals contacted to date have 

declined to participate in this study. This constitutes a participation rate of 

greater than 99 percent, which is unusually high for this type of research. 

Although a participation rate this high may be an artifact of the sampling 

method or of an organizational pressure to participate, it also may indicate a 

general desire within the Intelligence Community to support performance 

improvement research. 

Unlike random sampling, purposive sampling is an attempt to collect data 

from specific data sources. In anthropological studies, purposive sampling is 

regularly used to address specific issues and to answer specific questions. 

Normally, this approach requires finding a “key informant” or someone on the 

inside of a specific culture who will become the researcher’s ally and access 

agent. In this particular study, the CSI staff acted as access agents to the Intelligence 

Community at large. 

5 

American Anthropological Association, Code of Ethics of the American Anthropological Association. 

121

CHAPTER TEN 

Relying on such a “social network” sampling method for collecting interview 

data does pose potential statistical biases. 6 The likelihood that each new 

interviewee was referred to me because of a friendly relationship with a previous 

interviewee may mean that those references are “like minded” and not 

necessarily representative of the population of intelligence professionals. In 

order to counteract that bias, efforts were also made to enlist individuals without 

any social network-based introduction. The “cold” contacts were informed 

of the nature of the research project, its sponsorship, and its goals, given reference 

information for verification, and then invited to participate. The “cold” 

contact interviewees were also asked to make recommendations and provide 

contact information for others who might be interested in participating in the 

study. 

This strategy was used in an attempt to reduce the affects of sampling bias 

by generating parallel social network samples. The figure below is a visual 

representation of a parallel social-network sampling model. The central, or 

first-order, node on the left is a “cold” contact or unknown individual who recommends 

several second-order contacts, each represented as a node within the 

left box. The second-order “cold” contacts then make additional recommendations 

for third-order contacts, and so on. The central (first-order) node on the 

right is a “hot” contact or a known individual who recommends several second-order 

contacts, each represented as a node within the right box. The second-order 

“hot” contacts 

Social Network Mapping 

then make recommendations 

for third-order contacts, 

and so on. 

In many instances, the 

contacts from both social 

network samples overlapped 

or converged on 

specific individuals, as 

represented by the overlapped 

fourth-order nodes 

in the central column. 

There are several possible 

explanations for this convergence. 

It may indicate 

that there are a number of 

respected “thought leaders” in the Intelligence Community whom each contact 

believed I should interview for this project, or the convergence of nodes 

6 Social network sampling is also known as “snowball” sampling in sociology 

and psychology. 

122


might merely serve to emphasize the small size of Intelligence Community. In 

any case, this approach to sampling may help to ameliorate the sampling bias 

inherent in qualitative research. 

In addition to semi-structured interviews, both direct and participant observation 

data collection methods were employed. The direct observation method 

involved watching Intelligence Community analysts perform their tasks in 

both actual and training environments, recording the physical and verbal interactions 

they had with one another, and observing the steps used to create intelligence 

products. Direct observation occurred over the course of two years by 

observing 325 individual analysts and teams of analysts performing their specific 

tasks. The data collected from observing the 325 analysts were not 

included in the semi-structured interview data because I did not use the formal 

semi-structured interview process to structure those interactions. These observational 

data were recorded separately in field notes and used for triangulating 

the findings from the interviews. 

The participant observation method is employed to give the researcher a 

“first-person” understanding of the context and nuances associated with a task 

and the culture in which that task occurs. Although the researcher possesses 

only an approximation of the knowledge and understanding of the actual practitioners 

of the task and their culture, this “first-person” perspective can lead 

the researcher to new insights and new hypotheses. 

During this study, the participant observation was conducted during analytic 

production cycles, scenario development, and red cell exercises. This 

included monitoring my own analytic strategies, the analytic strategies of others 

as diagramed or verbalized, the physical and verbal social interactions 

among the participants, the environment in which the tasks occurred, and the 

steps used to create a final intelligence product. These data, along with notes 

on social dynamics, taboos, and social power, were recorded in field notes and 

created a separate data source for triangulation. 

With modern anthropology, these data normally would be captured on film, 

audiotape, or in some digital format. Due to the security requirements of the 

Intelligence Community, however, the data were captured only in the written 

form of field notes. As is the case with the field notes, the identity of the interview 

participants will not be disclosed. This is in keeping with both the security 

practices of the Intelligence Community and the professional standards 

described in the American Anthropological Association Statement on the 

Confidentiality of Field Notes. 7 

The data from the interviews were analyzed using a method called interpretational 

analysis. 8 This approach included segmenting the interview data into 

7 

American Anthropological Association, Statement on the Confidentiality of Field Notes. 

123

CHAPTER TEN 

analytic units (or units of meaning), developing categories, coding the analytic 

units into content areas, and grouping the analytic units into categories. From 

these categories, general trends and specific instances can be identified. As 

noted, the direct and participant observational data were analyzed separately 

in order to triangulate the findings from the interview data. The purpose of 

using multiple data sources for triangulation is to uncover internal inconsistencies 

in the data, to cross-check those inconsistencies with the available literature, 

and to verify the content validity for each category. 

Demographics 

As of this writing, 489 semi-structured interviews have been conducted with 

active and retired intelligence professionals, intelligence technology researchers, 

academics who teach the intelligence discipline or have published in it, and 

consumers of intel- 

Distribution of Interviews by Domain ligence products. 9 

Of the 489 individuals 

interviewed, 

4% 

11% Intelligence 70-percent were 

Academics 

15% 

Professionals 

newly hired, active, 

or retired intelligence 

Technology Researchers 

profession- 

als; 15-percent were 

70% 

Consumers 

academics; 11-percent 

were intelligence 

technology 

researchers; and the 

remaining four percent were policy makers or senior consumers of intelligence 

products. The graph here shows the distribution of interviews by percentage for 

each professional category. 

The table below lists each professional category and the corresponding total 

number (N) of individuals interviewed. The intelligence professional category 

is further divided into three sub-groups. The “novice” sub-group includes new 

hires and those with less than two years of experience. 10 The “active” sub- 

8 

Leonard Bickman and Debra Rog, Handbook of Applied Social Research Methods; Meredith 

Gall et al., Educational Research; Jonathan Gross, Measuring Culture: A Paradigm for the Analysis 

of Social Organization; Ernest House, Evaluating with Validity; Jerome Kirk and Marc 

Miller, Reliability and Validity in Qualitative Research, Qualitative Research Methods, Volume 1; 

Delbert Miller, Handbook of Research Design and Social Measurement; Michael Patton, Qualitative 

Evaluation and Research Methods; Peter Rossi and Howard Freeman, Evaluation. A Systematic 

Approach. 

9 

Additional interviews are being conducted. 

124


group includes all those currently working in the Intelligence Community 

with more than two years of experience. The “retired” sub-group includes 

those who have spent more than fifteen years in the intelligence profession 

and have since gone on to either full retirement or other organizations outside 

of the Intelligence Community. 

Of the 345 intelligence professionals interviewed, 20 percent were novices, 

65 percent were active, and 15 percent were retired. The active and retired 

sub-groups include senior managers. 

Interview Categories and Numbers 

Category 

N 

Intelligence Professionals 345 

Novice (60) 

Active (233) 

Retired (52) 

Academics 73 

Technology Researchers 53 

Consumers 18 

Total Interviewed489 

In order to assure anonymity for the participants, I have created broader jobrelated 

functional categories and associated the number of individuals interviewed 

with the broader categories rather than linking them to specific organizations 

within the Intelligence Community. This is in contrast to aggregating the 

agencies according to each agency’s specific mission, process, or product. 

Although not an official member of the Intelligence Community, the Drug 

Enforcement Administration is included because of its intelligence function and 

resources. The table on the next page shows how I aggregated the agencies into 

National-Technical, Defense, and Law Enforcement-Homeland Security categories 

according to the professional functions of interview participants. 

10 

The use of two years as a divide between novice and active is derived from the total amount of 

experience it is possible to gain in that time. See the discussion of expertise in Chapter Five. 

125

CHAPTER TEN 

Agency Aggregation According to Interviewee Job-type 

National-Technical Defense Law Enforcement- 

Homeland Security 

Central Intelligence Defense Intelligence Department of 

Agency Agency Homeland Security 

National Security Army Intelligence Federal Bureau of 

Agency 

Investigation 

National Air Force Intelligence Department of Energy 

Reconnaissance Office 

National Geospatial Navy Intelligence Department of Treasury 

Intelligence Agency 

Department of State Marine Corps Drug Enforcement 

(INR) Intelligence Administration 

The figure below shows the distribution of intelligence professionals interviewed 

for this study according to each broader functional category. Of the 

345 intelligence professionals interviewed, 214 work within the National- 

Technical Intelligence category, 76 in the Defense Intelligence category, and 

55 in the Law Enforcement—Homeland Security category. 

126

CHAPTER ELEVEN 

Q-Sort Methodology 

Observations always involve theory. 

Edwin Hubble 1 

As described earlier, this work reflects triangulation of the data derived 

from the literature Q-sort, interview responses, and observations. 2 The data 

include 489 interviews, direct and participant observation of 325 analysts performing 

their jobs, participation in a variety of analytic tasks, and focus 

groups conducted to generate the taxonomy of variables that guided this study. 

The first Q-sort of the data was aggregated according to the function of 

each intelligence organization, as listed in Table 1. The data were then analyzed 

to determine response context according to job type and to develop variable 

categories. 

The organizational Q-sort generated the broad variable groupings used to 

create the second Q-sort parameters. The variable categories that emerged 

during the interpretive analysis of the first Q-sort of the data were compiled 

again, and a second Q-sort was performed based on those categories. The data 

was then aggregated according to categorical or variable groupings of the second 

Q-sort, Table 2. 

The use of two separate Q-sort strategies generated the variables and then 

de-contextualized the data in order to find consistent trends throughout the 

Intelligence Community. That is, this strategy resulted in broad categories of 

findings that apply across many agencies. In those cases where interview and 

1 

Edwin Hubble discovered the first evidence to support the Big Bang theory that the universe is 

expanding and that the Milky Way is not the only galaxy in the universe. He also developed the 

Hubble Galaxy Classification System and Hubble’s Law (the farther away a galaxy is from Earth, 

the faster its motion away from Earth). Edwin Hubble, The Realm of the Nebulae. 

2 

William Stephenson, The Study of Behavior: Q-Technique and its Methodology. 

127

CHAPTER ELEVEN 

observational data could have been sorted into several categories, I based the 

placement of the data on the question that generated the interview response. 

Table 1. Q-Sort 1. Data Grouping According to Organizational Function. 

National – Technical Defense Law Enforcement – 

Homeland Security 

Central Intelligence Defense Intelligence Department of 

Agency Agency Homeland Security 

National Security Army Intelligence Federal Bureau of 

Agency 

Investigation 

National Air Force Intelligence Department of Energy 

Reconnaissance Office 

National Geospatial Navy Intelligence Department of the 

Intelligence Agency 

Treasury 

Department of State Marine Corps Drug Enforcement 

(INR) Intelligence Administration 

In several instances throughout the text, the quotes that were used may well 

fit in a number of other categories. Once the data were sorted by variable, the 

coding and context identifier notes were removed from all data in order to 

assure participant anonymity, in keeping with the American Anthropological 

Association Code of Ethics, section III, A. 3 

Table 2. Q-Sort 2. Data Grouping According to Variable Categories. 

Time 

Constraints 

Analytic 

Methods 


Norms 


Identity 



Products Tradecraft Taboos Reportorial Formal 

Interactions Science Biases Academic Informal 

The quotes that appear throughout the text are exemplars from each variable 

category and indicate trends found in the data-set. Although the exemplar 

quotes are not universal, nor are they necessarily subject to generalization, 

they do represent consistent findings from the interview and observation data. 

Utilizing this approach to develop theory is similar to the method in which 

grounded theory is employed in sociology, specifically, using grounded data 

to generate theory rather than using some a priori technique. The significant 

advantage to this approach is that the theory is directly tied to data, providing 

it additional validity. Another advantage is that the individuals who allowed 

me to interview and observe them are given some voice in the final product by 

way of direct quotes, which also provides some qualitative context. 

3 

American Anthropological Association, Code of Ethics of the American Anthropological Association. 

128

CHAPTER TWELVE 

The “File-Drawer” Problem and Calculation of Effect Size 

The file-drawer problem appears to have two causes: the reluctance of 

researchers to report their null results and the reluctance of professional journal 

editors to include studies whose results fail to reach statistical significance. 

Such studies remain in the “file-drawers” of the researchers. How 

much would these inaccessible studies affect the results of our meta-analysis? 

The answer seems to be not much. 1 

Effect Size - Difference Between Two Means 

1 

Gene Glass, and Barry McGaw, “Choice of the Metric for Effect Size in Meta-Analysis”; Larry 

Hedges, “Estimation of Effect Size from a Series of Independent Experiments”; Larry Hedges and 

Ingram Olkin, “Vote-Counting Methods in Research Synthesis.” 

129


Effect size is usually defined as the difference between the means of two 

groups divided by the standard deviation of the control group ⎛ Χ−Χc ⎞ .2 

⎜ ∆= e ⎟ 

⎝ σ c 

⎠ 

Effect sizes calculated in this way estimate the difference between two 

group means measured in control group standard deviations as seen in the figure 

above. Glass et al. suggest that the choice of the denominator is critical 

and that choices other than the control group standard deviation are defensible. 

3 However, they endorse the standard choice of using the control group 

standard deviation. 

Alternatively, Hedges and Olkin show that, for every effect size, both the 

bias and variance of its estimate are smaller when standard deviation is 

obtained by pooling the sample variance of two groups instead of using the 

control group standard deviation by itself. 4 An effect size based on a pooled 

standard deviation estimates the difference between two group means measured 

in standard deviations estimated for the full population from which both 

⎛ Χe − Χc ⎞ 

experimental and control groups are drawn: ⎜ g = ⎟ , 5 where 

⎝ S ⎠ 

2 2 6 

S is the pooled standard deviation: (Ne − 1)(Se ) + (Nc − 1)(Sc ) 

S = . 

e + N Nc − 2 

Most commentators suggest that effect sizes can be treated as descriptive 

statistics and entered into standard tests for statistical significance. Hedges 

and Olkin have shown that the error variance around estimates of effect size is 

inversely proportional to the sample size of the studies from which the effect 

sizes are drawn. If the effect size in any review is drawn from studies employing 

widely different sample sizes, then the heterogeneity of variance among 

effect sizes prohibits their use in conventional t-tests, analyses of variance, 

and other inferential tests. This is the case in most of these reviews; therefore, 

effect sizes reported in this study are treated only with descriptive statistics. 

The effect sizes for computer-based training range from 0.20 to 0.46 depending 

on the population. 7 The effect size for distance instruction (television) is 

Control Mean, σc = Control 

Standard Deviation 

3 

Gene Glass, Barry McGaw and Mary Lee Smith, Meta-Analysis in Social Research. 

4 

Larry Hedges and Ingram Olkin, Statistical Methods for Meta-Analysis. 

5 

g=Hedge's Effect Size, S=Hedge's Pooled Standard Deviation 

6 

Ne=Number of experimental subjects, Nc=Number of control subjects, Se=Standard deviation 

of experimental group, Sc=Standard deviation of control group 

2 

∆=Glass's Effect Size, Χe = Experimental Mean, Χc = 

130


0.15 and for interactive videodiscs, the effect sizes range from 0.17 to 0.66 

depending on the population. 8 The effect size for flight simulation is 0.54 and 

the effect size for tutorials range from 0.25 to 0.41 depending on the presentation 

of the tutorial material. 9 

Although the effect sizes for instructional technology range from 0.15 to 0.66 

standard deviations, they all report favorable findings when compared to conventional 

instruction. 

There are many possible 

explanations for 

the differences in 

instructional technology 

effectiveness; it 

might be the result of 

population differences, 

system differences, 

interactivity 

or individualization. 

From a purely utilitarian 

point of view, 

the reason may not 

be all that important. 

If, at the very least, 

using instructional 

technology forces the 

producer to rethink 

the content of the 

course to match the 

delivery system, then 

revisiting the pedagogy may be enough to produce the positive effect sizes. Whatever 

reason for the changes in effectiveness, the use of instructional technology 

saves instructional time, overhead costs, and results in a higher level of achievement 

for the students in a variety of domains. 

7 

The abbreviations in figure one: CBT=Computer Based Training, DI=Distance Instruction, 

IVD=Interactive Video Disc, SIM=Simulation. More than 300 research studies were used to 

develop these effect sizes, see Chen-Lin Kulik., James Kulik and Barbara Shwalb, “Effectiveness 

of Computer-Based Adult Education: A Meta-Analysis”; Chen-Lin Kulik and James Kulik, 

“Effectiveness of Computer-Based Education in Colleges”; Rob Johnston and J. Dexter Fletcher, 

A Meta-Analysis of the Effectiveness of Computer-Based Training for Military Instruction. 

8 

Godwin Chu and Wilbur Schramm, Learning from Television; J. Dexter Fletcher Effectiveness 

and Cost of Interactive Videodisc Instruction in Defense Training and Education; J. Dexter 

Fletcher, “Computer-Based Instruction: Costs and Effectiveness.” 

9 

R. T. Hays, J. W. Jacobs, C. Prince and E. Salas, “Flight Simulator Training Effectiveness: A Meta- 

Analysis”; Peter Cohen, James Kulik and Chen-Lin Kulik, “Educational Outcomes of Tutoring.” 

131

APPENDIX 

Selected Literature 

Intelligence Tools and Techniques 

Jerome K. Clauser and Sandra M. Weir, Intelligence Research Methodology: 

An Introduction to Techniques and Procedures for Conducting Research in 

Defense Intelligence (Washington, DC: US Defense Intelligence School, 

1976). 

Stanley Feder, “FACTIONS and Policon: New Ways to Analyze Politics,” 

(1987) in H. Bradford Westerfield, ed., Inside CIA’s Private World: Declassified 

Articles from the Agency’s Internal Journal (New Haven: Yale University 

Press, 1995). 

Craig Fleisher and Babette Bensoussan, Strategic and Competitive Analysis. 

Methods and Techniques for Analyzing Business Competition (Upper Saddle 

River, NJ: Pearson Education, 2003). 

Leonard Fuld, The New Competitor Intelligence. The Complete Resource for 

Finding, Analyzing, and Using Information About Your Competitors (New 

York: John Wiley & Sons, 1995). 

Ronald Garst, ed., A Handbook of Intelligence Analysis, 2nd ed (Washington, 

DC: Defense Intelligence College, 1989). 

R. Hopkins, Warnings of Revolution: A Case Study of El Salvador (Washington, 

DC: Center for the Study of Intelligence, 1980) TR 80-100012. 

Morgan Jones, The Thinker’s Toolkit: 14 Powerful Techniques for Problem 

Solving (New York, NY: Times Business, 1998). 

Larry Kahaner, Competitive Intelligence. How to Gather, Analyze, and Use 

Information to Move Your Business to the Top (New York: Touchstone, 

Simon and Schuster, 1996). 

133

APPENDIX 

Jonathan Lockwood and K. Lockwood, “The Lockwood Analytical Method 

for Prediction (LAMP),” Defense Intelligence Journal 3, no. 2 (1994): 47– 

74. 

Douglas J. MacEachin, et al., The Tradecraft of Analysis: Challenge and 

Change in the CIA (Washington, DC: Consortium for the Study of Intelligence, 

1994). 

Don McDowell, Strategic Intelligence: A Handbook for Practitioners, Managers 

and Users (Canberra, Australia: Istana Enterprises, 1998). 

Marilyn Peterson, Applications in Criminal Analysis (Westport, CT: Greenwood 

Press, 1995). 

———, Bob Morehouse, and Richard Wright, eds. Intelligence 2000: Revising 

the Basic Elements (Sacramento CA: LEIU and IALEIA, 2000). 

John Pierce, “Some Mathematical Methods for Intelligence Analysis,” Studies 

in Intelligence 21 (Summer 1977, declassified): 1–19. 

John Prescott and Stephen Miller, eds. Proven Strategies in Competitive Intelligence 

(New York: John Wiley & Sons, 2001). 

Edwin Sapp, “Decision Trees,” Studies in Intelligence 18 (Winter 1974, 

declassified): 45–57. 

David Schum, The Evidential Foundations of Probabilistic Reasoning (Evanston, 

IL: Northwestern University Press, 1994). 

Jack Zlotnick, “Bayes’ Theorem for Intelligence Analysis,” (1972), in H. 

Bradford Westerfield, ed. Inside CIA’s Private World: Declassified Articles 

from the Agency’s Internal Journal (New Haven: Yale University Press, 

1995). 

Cognitive Processes and Intelligence 

Edgar Johnson, Effects of Data Source Reliability on Inference (Alexandria, 

VA: Army Research Institute for the Behavioral and Social Sciences, Technical 

Paper #251, 1974). 

Robert Mathams, “The Intelligence Analyst’s Notebook,” in Douglas Dearth 

and R. Thomas Goodden, eds., Strategic Intelligence: Theory and Application, 

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Avi Shlaim, “Failures in National Intelligence Estimates: The Case of the Yom 

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Tools and Techniques as Cognitive Processes 

Bruce D. Berkowitz and Allan E. Goodman, Best Truth: Intelligence in the 

Information Age (New Haven: Yale University Press, 2000). 

———, and Allan E. Goodman, Strategic Intelligence for American National 

Security (Princeton: Princeton University Press, 1989). 

134

APPENDIX 

Sherman Kent, “Words of Estimative Probability,” Sherman Kent and the 

Board of National Estimates: Collected Essays (Washington, DC: Center for 

the Study of Intelligence, 1994). 

Lisa Krizan, Intelligence Essentials for Everyone (Washington, DC: Joint Military 

Intelligence College, 1999). 

Intelligence Analysis as Individual Cognitive Process 

Alexander Butterfield, The Accuracy of Intelligence Assessment: Bias, Perception, 

and Judgment in Analysis and Decision (Newport, RI: Naval War 

College, 1993). 

Richards J. Heuer, Jr., Psychology of Intelligence Analysis (Washington, DC: 

Center for the Study of Intelligence, 1999). 

Robert Jervis, Perception and Misperception in International Politics (Princeton: 

Princeton University Press, 1977). 

Ephraim Kam, Surprise Attack: The Victim’s Perspective (Cambridge, MA: 

Harvard University Press, 1988). 

Geraldine Krotow, The Impact of Cognitive Feedback on the Performance of 

Intelligence Analysts (Monterey, CA: Naval Postgraduate School, 1992). 

Mark Lowenthal, Intelligence: From Secrets to Policy (Washington, DC: CQ, 

2000). 

David Moore, Creating Intelligence: Evidence and Inference in the Analysis 

Process. Unpublished Masters Thesis (Washington DC: Joint Military Intelligence 

College, 2002). 

J. R. Thompson, R. Hopf-Weichel, and R. Geiselman, The Cognitive Bases of 

Intelligence Analysis (Alexandria, VA: Army Research Institute, Research 

Report 1362, 1984). 

Error 

Daniela Busse and Chris Johnson “Using a Cognitive Theoretical Framework 

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Peter Fishburn, Utility Theory for Decision Making (New York: John Wiley, 

1970). 

Paul Fitts and Michael Posner, Human Performance (Belmont, CA: Brooks 

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John Hammond, Ralph Keeney, and Howard Raiffa, “The Hidden Traps in 

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135

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Hede Helfrich, “Human Reliability from a Social-Psychological Perspective,” 

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Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of 

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James Reason, Human Error (New York: Cambridge University Press, 1990). 

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Neville Stanton and Sarah Stevenage, “Learning to Predict Human Error: 

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Alfred Bloom, The Linguistic Shaping of Thought: A Study in the Impact of 

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Lera Boroditsky, “Does Language Shape Thought? Mandarin and English 

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Ernest Cassirer, “Le Langage et le Monde des Objets,” Journal de Psychologie 

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136

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Penny Lee, The Whorf Theory Complex: A Critical Reconstruction (Amsterdam: 

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of New York, 1998. 

156

BIBLIOGRAPHY 

Web resources 

Buros Institute of Mental Measurements (http://www.unl.edu/buros/). 

Central Intelligence Agency main website (http://www.cia.gov). 

Central Intelligence Agency Office of General Council (http://www.cia.gov/ 

ogc/best.htm). 

Central Intelligence Agency Officer in Residence Program (http:// 

www.cia.gov//csi/officer.html). 

Defense Intelligence Agency (http://www.dia.mil). 

Federal Bureau of Investigation (http://www.fbijobs.com or http:// 

www.fbi.gov). 

Human Relations Area Files. Yale University. (http://www.yale.edu/hraf/). 

National Aeronautics and Space Administration-Johnson Space Center career 

astronaut biography. (http://www.jsc.nasa.gov/Bios/astrobio 

_activemgmt.html). 

National Imagery and Mapping Agency (http://www.nima.mil/). 

National Reconnaissance Office (http://www.nro.gov/). 

National Security Agency (http://www.nsa.gov/). 

US Air Force (http://aia.lackland.af.mil/aia/). 

US Army (http://usaic.hua.army.mil/index.htm). 

U.S. Army Center for Army Lessons Learned (http://call.army.mil/). 

US Coast Guard (http://www.uscg.mil/USCG.shtm). 

US Department of Energy (http://www.energy.gov/engine/content.do). 

US Department of Homeland Security (http://www.dhs.gov/dhspublic/). 

US Department of State (http://www.state.gov/s/inr/). 

US Department of the Treasury (http://www.treasury.gov/). 

US Intelligence Community (http://www.intelligence.gov/index.shtml). 

US Marine Corps (http://www.usmc.mil/). 

US Navy (http://www.nmic.navy.mil/). 

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AFTERWORD 

Joseph Hayes 1 

The unexamined life is not worth living 

Socrates. 

A very popular error: having the courage of one’s convictions; 

rather, it is a matter of having the courage for an attack on one’s 

convictions! 

Nietzsche 

Rob Johnston has written a superb book, a study of intelligence as it is actually 

practiced. Rob’s book is alive with specific, practical recommendations 

about how the practice of intelligence could be made better. The literature of 

intelligence is overwhelmingly devoted either to studies which, however rigorous 

in their academic structure, fail to convey the humanness of the enterprise 

or to books and articles, too often self congratulatory or self promoting, 

which are little more than assemblages of entertaining anecdotes. Rob’s study 

deserves a place of honor on the very small bookshelf reserved for analytically 

sound, deeply insightful works on the conduct of intelligence. Any serious 

discussion of reform or significant change in the ways in which US intelligence 

is organized, structured, and carried out will need to take this book as a 

starting point. 

Rob’s work bears witness to the imagination and commitment to excellence 

on the part of the senior intelligence officials who made it possible for a cultural 

anthropologist to carry out his field work in the secret, sometimes hermetically 

1 

Joseph Hayes is a retired senior officer of the Central Intelligence Agency Directorate of Operations. 

He served more than 30 years in the clandestine service. 

159

sealed, world of intelligence. Rob’s work also bears witness to the tremendous 

passion among intelligence professionals to understand better what they do, why 

they do it, and how their work could be improved. There is, in this community, a 

palpable desire to do better. 

Since the tragedy of 9/11 and the bitter controversies surrounding Iraqi WMD, 

the world of intelligence analysis has been scrutinized with an intensity of almost 

unprecedented dimensions. The focus of scrutiny, however, has been on the 

results, not on the process by which the results were produced and certainly not 

on the largely anonymous corps of civil servants whose work was at the heart of 

the issue. Those people, and how and why they do what they do, are at the heart 

of Rob’s important study. If we are ever to make the improvements that must be 

made in the quality of our intelligence work, then we must begin with a more 

mature and nuanced understanding of who actually does the work, how, and why. 

There is a context within which the work is done, a definite culture with values, 

traditions, and procedures that help shape important outcomes. 

Rob has characterized a world that I find all too familiar. It is a world in which 

rewards and incentives are weighted heavily in favor of filling in gaps in conventional 

knowledge rather than in leading the way to alternative points of view. It is 

a world in which confirming evidence is welcome and rewarded and disconfirmatory 

evidence is, at best, unwelcome and, at worst, discounted. It is a world in 

which the legitimate and often necessary resort to secrecy has served, all too 

often, to limit debate and discussion. It is a world in which the most fundamentally 

important questions—what if and why not—are too often seen as distractions 

and not as invitations to rethink basic premises and assumptions. 

Much of the recent discussion concerning the performance of intelligence 

organizations has been conducted in almost mechanical terms. “Connecting 

the dots,” “mining the nuggets” are phrases offered as a way of understanding 

the exquisitely subtle, complex business of making intelligence judgments. 

As Rob Johnston’s book makes abundantly clear, this is first and foremost a 

human enterprise. All of the intellectual power, biases, and paradigms that 

inform the thinking of the people who actually do the work need to be understood 

in the organizational context in which they do their work. In the finest 

tradition of anthropological field research, Rob has observed, collected data 

rigorously, reflected with deep insight upon it, and produced a study both 

sophisticated and extremely useful. 

I worked myself for more than 30 years in the clandestine operations area of 

CIA, a part of the Intelligence Community that calls out for the same kind of 

understanding, professional, and constructive scrutiny this book has devoted to 

the analytic realm. My fervent hope is that the human intelligence service will 

benefit from the same kind of rigor and constructive understanding the analytic 

side has now experienced. My real hope is that Rob is available for the job. 

160

The Author 

Dr. Rob Johnston is an ethnographer who specializes in the cultural anthropology 

of work. He has been a research staff member at the Institute for 

Defense Analyses and a Director of Central Intelligence Postdoctoral 

Research Fellow at the Central Intelligence Agency’s Center for the Study of 

Intelligence, where he is now a member of the staff. 

Dr. Johnston is a Fellow of the Royal Anthropological Institute, the Society 

for Applied Anthropology, and the Inter-University Seminar on Armed Forces 

and Society. 

161

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